├── challenge_1 ├── max_PTS_of_year.csv ├── double_double.csv ├── best_player.csv └── NBA.ipynb ├── README.md ├── LICENSE ├── challenge_3 ├── submission.csv └── Student_GPA.ipynb ├── challenge_2 ├── submission.csv └── Authors.ipynb ├── challenge_5 └── submission.csv └── challenge_4 └── ChandMidi.ipynb /challenge_1/max_PTS_of_year.csv: -------------------------------------------------------------------------------- 1 | YEAR,TEAM,PTS 2 | 2000,SAC,100.2 3 | 2001,LAL,95.9 4 | 2002,ORL,97.0 5 | 2003,DEN,96.0 6 | 2004,BOS,106.2 7 | 2005,WAS,96.8 8 | 2006,PHX,105.4 9 | 2007,DEN,110.7 10 | 2008,DEN,97.3 11 | 2009,OKC,102.5 12 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # TechOlympics AI NLP Competition 2 | 3 | I participated in the **TechOlympics AI NLP** competition hosted on [Quera](https://quera.org/events/techolympics-ai-0307), where I ranked 50th out of 400 participants. The competition consisted of five challenging problems related to Natural Language Processing (NLP). 4 | 5 | ## Problem Links: 6 | 1. [Problem 1](https://quera.org/contest/assignments/71153/problems) 7 | 2. [Problem 2](https://quera.org/contest/assignments/71153/problems/250608) 8 | 3. [Problem 3](https://quera.org/contest/assignments/71153/problems/250609) 9 | 4. [Problem 4](https://quera.org/contest/assignments/71153/problems/250605) 10 | 5. [Problem 5](https://quera.org/contest/assignments/71153/problems/250606) 11 | 12 | ## Achievements: 13 | - **Rank**: 50th out of 400 participants 14 | - **Focus Area**: Natural Language Processing (NLP) 15 | - **Competition Date**: [1403/7/5] 16 | 17 | This README serves as a brief overview of my participation and the results I achieved during the competition. 18 | 19 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2024 Alireza Parvaresh 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /challenge_1/double_double.csv: -------------------------------------------------------------------------------- 1 | PLAYER,YEAR,PTS,AST,REB,BLK,STL 2 | Antonio Davis,2000,13.7,1.4,10.1,1.9,0.3 3 | Antonio McDyess,2000,20.8,2.1,12.0,1.5,0.6 4 | Chris Webber,2000,27.1,4.2,11.1,1.7,1.3 5 | Dikembe Mutombo,2000,10.0,1.0,13.5,2.7,0.4 6 | Elton Brand,2000,20.1,3.2,10.1,1.6,1.0 7 | Kevin Garnett,2000,22.0,5.0,11.399999999999999,1.8,1.4 8 | Shaquille O'Neal,2000,28.7,3.7,12.700000000000001,2.8,0.6 9 | Shawn Marion,2000,17.3,2.0,10.7,1.4,1.7 10 | Tim Duncan,2000,22.2,3.0,12.2,2.3,0.9 11 | Andre Miller,2001,16.5,10.9,4.6,0.4,1.6 12 | Danny Fortson,2001,11.2,1.6,11.7,0.2,0.6 13 | Dikembe Mutombo,2001,11.5,1.0,10.8,2.4,0.4 14 | Dirk Nowitzki,2001,23.4,2.4,10.0,1.0,1.1 15 | Elton Brand,2001,18.2,2.4,11.6,2.0,1.0 16 | Jermaine O'Neal,2001,19.0,1.6,10.5,2.3,0.6 17 | Kevin Garnett,2001,21.2,5.2,12.1,1.6,1.2 18 | Shaquille O'Neal,2001,27.2,3.0,10.7,2.0,0.6 19 | Tim Duncan,2001,25.5,3.7,12.7,2.5,0.7 20 | Brian Grant,2002,10.3,1.3,10.2,0.6,0.8 21 | Chris Webber,2002,23.0,5.4,10.5,1.3,1.6 22 | Jermaine O'Neal,2002,20.8,2.0,10.3,2.3,0.9 23 | Kevin Garnett,2002,23.0,6.0,13.5,1.6,1.4 24 | Shaquille O'Neal,2002,27.5,3.1,11.1,2.4,0.6 25 | Tim Duncan,2002,23.3,3.9,12.899999999999999,2.9,0.7 26 | Troy Murphy,2002,11.7,1.3,10.2,0.4,0.8 27 | Brad Miller,2003,14.1,4.3,10.4,1.2,0.9 28 | Carlos Boozer,2003,15.5,2.0,11.5,0.7,1.0 29 | Erick Dampier,2003,12.3,0.8,11.899999999999999,1.9,0.4 30 | Jamaal Magloire,2003,13.6,1.0,10.399999999999999,1.2,0.5 31 | Jermaine O'Neal,2003,20.1,2.1,10.0,2.6,0.8 32 | Kenny Thomas,2003,13.6,1.5,10.1,0.4,1.1 33 | Kevin Garnett,2003,24.2,5.0,13.9,2.2,1.5 34 | Shaquille O'Neal,2003,21.5,2.9,11.5,2.5,0.5 35 | Tim Duncan,2003,22.3,3.1,12.5,2.7,0.9 36 | Zach Randolph,2003,20.1,2.0,10.5,0.5,0.8 37 | Dwight Howard,2004,12.0,0.9,10.0,1.7,0.9 38 | Emeka Okafor,2004,15.1,0.9,10.899999999999999,1.7,0.8 39 | Kevin Garnett,2004,22.2,5.7,13.5,1.4,1.5 40 | Kurt Thomas,2004,11.5,2.0,10.4,1.0,0.9 41 | Shaquille O'Neal,2004,22.9,2.7,10.4,2.3,0.5 42 | Shawn Marion,2004,19.4,1.9,11.3,1.5,2.0 43 | Steve Nash,2004,15.5,11.5,3.4000000000000004,0.1,1.0 44 | Troy Murphy,2004,15.4,1.4,10.8,0.5,0.8 45 | Dwight Howard,2005,15.8,1.5,12.5,1.4,0.8 46 | Elton Brand,2005,24.7,2.6,10.0,2.5,1.0 47 | Kevin Garnett,2005,21.8,4.1,12.7,1.4,1.4 48 | Shawn Marion,2005,21.8,1.8,11.9,1.7,2.0 49 | Steve Nash,2005,18.8,10.5,4.2,0.2,0.8 50 | Tim Duncan,2005,18.6,3.2,11.0,2.0,0.9 51 | Troy Murphy,2005,14.0,1.4,10.0,0.4,0.6 52 | Carlos Boozer,2006,20.9,3.0,11.7,0.3,0.9 53 | Chris Bosh,2006,22.6,2.5,10.7,1.3,0.6 54 | Dwight Howard,2006,17.6,1.9,12.3,1.9,0.9 55 | Kevin Garnett,2006,22.4,4.1,12.8,1.7,1.2 56 | Marcus Camby,2006,11.2,3.2,11.600000000000001,3.3,1.2 57 | Steve Nash,2006,18.6,11.6,3.5,0.1,0.8 58 | Tim Duncan,2006,20.0,3.4,10.600000000000001,2.4,0.8 59 | Zach Randolph,2006,23.6,2.2,10.1,0.2,0.8 60 | Al Jefferson,2007,21.0,1.4,11.2,1.5,0.9 61 | Antawn Jamison,2007,21.4,1.5,10.2,0.4,1.3 62 | Carlos Boozer,2007,21.1,2.9,10.4,0.5,1.2 63 | Chris Paul,2007,21.1,11.6,4.0,0.1,2.7 64 | Deron Williams,2007,18.8,10.5,2.9,0.3,1.1 65 | Dwight Howard,2007,20.7,1.3,14.200000000000001,2.1,0.9 66 | Emeka Okafor,2007,13.8,0.9,10.7,1.7,0.8 67 | Jason Kidd,2007,10.8,10.1,7.5,0.3,1.7 68 | Lamar Odom,2007,14.2,3.5,10.7,0.9,1.0 69 | Samuel Dalembert,2007,10.5,0.5,10.3,2.3,0.5 70 | Steve Nash,2007,16.9,11.1,3.4,0.1,0.7 71 | Tim Duncan,2007,19.3,2.8,11.3,1.9,0.7 72 | Tyson Chandler,2007,11.8,1.0,11.8,1.1,0.6 73 | Chris Bosh,2008,22.7,2.5,10.0,1.0,0.9 74 | Chris Paul,2008,22.8,11.0,5.6000000000000005,0.1,2.8 75 | David Lee,2008,16.0,2.1,11.8,0.3,1.0 76 | Dwight Howard,2008,20.6,1.4,13.899999999999999,2.9,1.0 77 | Emeka Okafor,2008,13.2,0.6,10.1,1.7,0.6 78 | Tim Duncan,2008,19.3,3.5,10.7,1.7,0.5 79 | Troy Murphy,2008,14.3,2.4,11.8,0.5,0.8 80 | Carlos Boozer,2009,19.5,3.2,11.2,0.5,1.1 81 | Chris Bosh,2009,24.0,2.4,10.8,1.0,0.6 82 | David Lee,2009,20.2,3.6,11.7,0.5,1.0 83 | Deron Williams,2009,18.7,10.5,4.0,0.2,1.3 84 | Dwight Howard,2009,18.3,1.8,13.2,2.8,0.9 85 | Gerald Wallace,2009,18.2,2.1,10.1,1.1,1.5 86 | Steve Nash,2009,16.5,11.0,3.3,0.1,0.5 87 | Tim Duncan,2009,17.9,3.2,10.1,1.5,0.6 88 | Troy Murphy,2009,14.6,2.1,10.200000000000001,0.5,1.0 89 | Zach Randolph,2009,20.8,1.8,11.8,0.4,1.0 90 | -------------------------------------------------------------------------------- /challenge_3/submission.csv: -------------------------------------------------------------------------------- 1 | GPA 2 | 1.482846762592041 3 | 3.0673033920392423 4 | 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2.463407620091446 450 | 0.9473053170569335 451 | 0.5260393575136844 452 | 2.44019618598961 453 | 0.6154586423606392 454 | 2.7034470289529975 455 | 2.3670654117685705 456 | 3.2675055453486452 457 | 1.834110395338828 458 | 1.2488368791166344 459 | 1.0016764822799757 460 | 0.4842475456028901 461 | 2.136417492570676 462 | 1.257028802568553 463 | 0.618014270458221 464 | 0.7950736299738178 465 | 1.5861139994106621 466 | 2.392346851356694 467 | 0.5476431432271613 468 | 0.4521093520379611 469 | 2.664870215936163 470 | 3.1142290361840237 471 | 2.31722194101731 472 | 1.1547800578928102 473 | 1.8910801316550623 474 | 1.4929338040749647 475 | 1.4153500031969828 476 | 1.6815802327049993 477 | 2.3671001756279355 478 | 2.7455138772331047 479 | 2.339564094405887 480 | 0.7224852272656764 481 | -------------------------------------------------------------------------------- /challenge_1/best_player.csv: -------------------------------------------------------------------------------- 1 | PLAYER,Value 2 | Kevin Garnett,29.74 3 | LeBron James,27.9 4 | Chris Paul,26.57 5 | Shaquille O'Neal,26.55 6 | Dwyane Wade,26.12 7 | Dirk Nowitzki,26.09 8 | Tim Duncan,26.09 9 | Kobe Bryant,25.3 10 | Chris Webber,24.93 11 | Tracy McGrady,23.81 12 | Dwight Howard,23.68 13 | Amar'e Stoudemire,23.52 14 | Karl Malone,23.5 15 | Elton Brand,23.25 16 | Shawn Marion,23.25 17 | Allen Iverson,22.94 18 | Chris Bosh,22.81 19 | Carlos Boozer,22.62 20 | David Lee,22.3 21 | Kevin Durant,22.1 22 | Gilbert Arenas,22.08 23 | Pau Gasol,22.06 24 | Paul Pierce,21.38 25 | Jason Kidd,21.31 26 | Marcus Camby,21.18 27 | Steve Nash,20.97 28 | Yao Ming,20.72 29 | Carmelo Anthony,20.66 30 | Brandon Roy,20.6 31 | Vince Carter,20.39 32 | Jermaine O'Neal,20.16 33 | Jamal Mashburn,19.8 34 | Lamar Odom,19.8 35 | Antawn Jamison,19.71 36 | Sam Cassell,19.62 37 | Tyreke Evans,19.6 38 | Brook Lopez,19.5 39 | Terrell Brandon,19.5 40 | Stephon Marbury,19.38 41 | Baron Davis,19.34 42 | Steve Francis,19.26 43 | Ray Allen,19.14 44 | LaMarcus Aldridge,19.1 45 | Gerald Wallace,19.05 46 | Glenn Robinson,19.0 47 | Stephen Curry,19.0 48 | Al Horford,18.85 49 | Michael Jordan,18.7 50 | Deron Williams,18.65 51 | Ben Wallace,18.64 52 | Corey Maggette,18.6 53 | Anthony Mason,18.55 54 | Zach Randolph,18.54 55 | David Robinson,18.5 56 | Monta Ellis,18.47 57 | Peja Stojakovic,18.47 58 | Andre Iguodala,18.33 59 | Jerry Stackhouse,18.23 60 | Shareef Abdur-Rahim,18.17 61 | Josh Smith,18.16 62 | Brad Miller,18.14 63 | Michael Redd,18.12 64 | Gary Payton,18.07 65 | David West,18.0 66 | Luol Deng,17.93 67 | Andre Miller,17.91 68 | Emeka Okafor,17.9 69 | Derrick Rose,17.85 70 | Andris Biedrins,17.8 71 | John Stockton,17.77 72 | Zydrunas Ilgauskas,17.75 73 | Al Jefferson,17.43 74 | Josh Howard,17.33 75 | Manu Ginobili,17.28 76 | Rashard Lewis,17.24 77 | Rasheed Wallace,17.19 78 | Danny Granger,17.1 79 | Chauncey Billups,17.02 80 | Danny Fortson,17.0 81 | Troy Murphy,16.93 82 | Carl Landry,16.9 83 | Andrew Bogut,16.85 84 | Nene,16.75 85 | Jason Richardson,16.48 86 | Antoine Walker,16.47 87 | Joe Johnson,16.41 88 | Kenyon Martin,16.4 89 | Russell Westbrook,16.35 90 | Kevin Martin,16.33 91 | Keith Van Horn,16.17 92 | Richard Jefferson,16.17 93 | Vlade Divac,16.15 94 | Luis Scola,16.13 95 | Jason Terry,16.11 96 | Elden Campbell,16.05 97 | Mike Bibby,16.04 98 | Andrei Kirilenko,16.0 99 | Tony Parker,15.93 100 | Marc Gasol,15.9 101 | Jalen Rose,15.88 102 | Charles Oakley,15.8 103 | Mehmet Okur,15.8 104 | Rajon Rondo,15.8 105 | Caron Butler,15.75 106 | Richard Hamilton,15.66 107 | Nick Van Exel,15.53 108 | Rudy Gay,15.52 109 | Clar. Weatherspoon,15.3 110 | Delonte West,15.3 111 | Kevin Love,15.3 112 | Mike Miller,15.25 113 | T.J. Ford,15.23 114 | Wesley Person,15.2 115 | Raef LaFrentz,15.18 116 | Allan Houston,15.13 117 | Kirk Hinrich,15.1 118 | Metta World Peace,15.1 119 | Brian Grant,15.02 120 | O.J. Mayo,15.0 121 | Damon Stoudamire,14.98 122 | Doug Christie,14.98 123 | Samuel Dalembert,14.95 124 | Tyson Chandler,14.93 125 | Eddie Jones,14.92 126 | P.J. Brown,14.9 127 | Grant Hill,14.83 128 | Antonio Davis,14.8 129 | Ricky Davis,14.67 130 | Danilo Gallinari,14.6 131 | Donyell Marshall,14.6 132 | Jason Thompson,14.6 133 | Jose Calderon,14.6 134 | Drew Gooden,14.58 135 | Chris Kaman,14.55 136 | Cuttino Mobley,14.54 137 | Tayshaun Prince,14.52 138 | Theo Ratliff,14.5 139 | Raymond Felton,14.48 140 | Mo Williams,14.45 141 | Dikembe Mutombo,14.42 142 | Alonzo Mourning,14.25 143 | Juwan Howard,14.21 144 | Jim Jackson,14.2 145 | Kenny Thomas,14.18 146 | Rodney Stuckey,14.1 147 | Wilson Chandler,14.1 148 | Ben Gordon,14.06 149 | Al Harrington,14.05 150 | Primoz Brezec,14.0 151 | Udonis Haslem,14.0 152 | Boris Diaw,13.95 153 | Wally Szczerbiak,13.93 154 | Jamal Crawford,13.92 155 | Tim Hardaway,13.9 156 | Paul Millsap,13.88 157 | Reggie Miller,13.85 158 | Antonio McDyess,13.83 159 | Josh Childress,13.83 160 | Brandon Jennings,13.8 161 | Eric Gordon,13.8 162 | Kurt Thomas,13.8 163 | Michael Beasley,13.75 164 | Jeff Green,13.73 165 | Ronnie Brewer,13.7 166 | Joe Smith,13.68 167 | Derek Anderson,13.67 168 | Chris Wilcox,13.5 169 | Latrell Sprewell,13.5 170 | Jamaal Tinsley,13.47 171 | Stephen Jackson,13.47 172 | Kerry Kittles,13.45 173 | Alvin Williams,13.4 174 | Charlie Villanueva,13.3 175 | Kenny Anderson,13.3 176 | Taj Gibson,13.3 177 | Mike Dunleavy,13.22 178 | Andrea Bargnani,13.2 179 | Rafer Alston,13.16 180 | Horace Grant,13.15 181 | Anderson Varejao,13.13 182 | Christian Laettner,13.1 183 | Darren Collison,13.1 184 | Tyrone Hill,13.1 185 | Hedo Turkoglu,13.09 186 | Erick Dampier,13.04 187 | Matt Harpring,13.0 188 | Lamond Murray,12.9 189 | Michael Finley,12.9 190 | Shane Battier,12.86 191 | Dale Davis,12.85 192 | Bonzi Wells,12.82 193 | Darrell Armstrong,12.8 194 | Devin Harris,12.8 195 | Rod Strickland,12.8 196 | Leandro Barbosa,12.78 197 | Mike Conley,12.75 198 | Aaron Brooks,12.6 199 | Jason Hart,12.5 200 | Luke Ridnour,12.46 201 | Ryan Gomes,12.45 202 | Dan Dickau,12.4 203 | Michael Dickerson,12.4 204 | Andray Blatche,12.37 205 | Kendrick Perkins,12.32 206 | Jamario Moon,12.3 207 | Nick Collison,12.28 208 | Aaron McKie,12.23 209 | David Wesley,12.2 210 | Ersan Ilyasova,12.2 211 | Keon Clark,12.2 212 | LaPhonso Ellis,12.2 213 | Larry Hughes,12.2 214 | Patrick Ewing,12.2 215 | Thaddeus Young,12.2 216 | Joakim Noah,12.15 217 | Lorenzen Wright,12.15 218 | Josh Boone,12.1 219 | Speedy Claxton,12.1 220 | Jamaal Magloire,12.07 221 | Marvin Williams,12.03 222 | Marcus Thornton,12.0 223 | Rodney Rogers,12.0 224 | Eddy Curry,11.94 225 | Anthony Parker,11.9 226 | Jonas Jerebko,11.9 227 | Brendan Haywood,11.87 228 | Mark Jackson,11.87 229 | Nenad Krstic,11.87 230 | Brent Barry,11.86 231 | Kelenna Azubuike,11.85 232 | Darius Miles,11.83 233 | Andrew Bynum,11.8 234 | Bobby Simmons,11.8 235 | Michael Olowokandi,11.8 236 | Jeff McInnis,11.76 237 | Popeye Jones,11.7 238 | Scot Pollard,11.7 239 | Nate Robinson,11.67 240 | Mike James,11.66 241 | Travis Outlaw,11.65 242 | Al Thornton,11.6 243 | Brian Cardinal,11.6 244 | Jarrett Jack,11.56 245 | Jason Williams,11.56 246 | John Salmons,11.55 247 | Ramon Sessions,11.5 248 | Ruben Patterson,11.46 249 | Smush Parker,11.35 250 | Joel Przybilla,11.3 251 | Maurice Taylor,11.3 252 | Rick Fox,11.3 253 | Tim Thomas,11.3 254 | James Posey,11.28 255 | Mark Blount,11.28 256 | Anfernee Hardaway,11.25 257 | Bryant Reeves,11.2 258 | DeJuan Blair,11.2 259 | Desmond Mason,11.2 260 | Kwame Brown,11.2 261 | Vin Baker,11.2 262 | Nazr Mohammed,11.18 263 | Matt Barnes,11.12 264 | Hakim Warrick,11.08 265 | Trevor Ariza,11.03 266 | Jonny Flynn,11.0 267 | Quentin Richardson,11.0 268 | Roy Hibbert,10.95 269 | Randy Foye,10.93 270 | Clifford Robinson,10.92 271 | Chris Whitney,10.9 272 | Chris Duhon,10.88 273 | Chucky Atkins,10.8 274 | Glen Rice,10.8 275 | Jerome Williams,10.8 276 | Spencer Hawes,10.77 277 | Channing Frye,10.73 278 | Zaza Pachulia,10.7 279 | Jumaine Jones,10.68 280 | Earl Boykins,10.65 281 | Kelvin Cato,10.65 282 | Chris Andersen,10.63 283 | JR Smith,10.58 284 | Rasho Nesterovic,10.57 285 | Shawn Bradley,10.53 286 | Morris Peterson,10.51 287 | Bo Outlaw,10.5 288 | Bryant Stith,10.5 289 | C.J. Watson,10.5 290 | Eric Snow,10.5 291 | Andres Nocioni,10.46 292 | Chris Gatling,10.4 293 | JJ Hickson,10.4 294 | Jameer Nelson,10.4 295 | Bob Sura,10.3 296 | Luc Mbah a Moute,10.3 297 | Michael Smith,10.3 298 | Michael Sweetney,10.3 299 | Jeff Foster,10.29 300 | Eddie Griffin,10.28 301 | Darko Milicic,10.25 302 | Dominic McGuire,10.2 303 | Kyle Lowry,10.2 304 | Mario Chalmers,10.2 305 | Tony Delk,10.2 306 | Derek Fisher,10.19 307 | Corliss Williamson,10.18 308 | Tyrus Thomas,10.13 309 | George McCloud,10.1 310 | Jon Barry,10.1 311 | Omri Casspi,10.1 312 | Rudy Fernandez,10.1 313 | Vladimir Radmanovic,10.05 314 | Courtney Lee,10.0 315 | Lou Williams,10.0 316 | DeShawn Stevenson,9.98 317 | Chris Mihm,9.97 318 | Voshon Lenard,9.97 319 | Earl Watson,9.93 320 | Travis Best,9.92 321 | Aaron Williams,9.9 322 | Amir Johnson,9.9 323 | Lee Nailon,9.9 324 | Arvydas Sabonis,9.8 325 | Brandon Bass,9.8 326 | James Harden,9.8 327 | Tyronn Lue,9.8 328 | Carlos Delfino,9.77 329 | Kyle Korver,9.77 330 | Antonio Daniels,9.75 331 | Craig Smith,9.75 332 | Etan Thomas,9.75 333 | Marcus Fizer,9.75 334 | Ronny Turiaf,9.73 335 | Dion Glover,9.7 336 | Serge Ibaka,9.7 337 | Toni Kukoc,9.7 338 | Jahidi White,9.6 339 | Jay Williams,9.6 340 | Leon Powe,9.6 341 | Robert Horry,9.58 342 | Anthony Carter,9.57 343 | Dan Gadzuric,9.57 344 | Mickael Pietrus,9.55 345 | Matt Bonner,9.53 346 | Eduardo Najera,9.5 347 | Greg Ostertag,9.48 348 | Francisco Garcia,9.45 349 | Eric Piatkowski,9.4 350 | Jrue Holiday,9.4 351 | Terrence Williams,9.4 352 | Jason Maxiell,9.33 353 | Linas Kleiza,9.33 354 | Ervin Johnson,9.3 355 | Fred Hoiberg,9.25 356 | Malik Rose,9.25 357 | Raja Bell,9.25 358 | Marko Jaric,9.23 359 | Steven Smith,9.2 360 | Vladimir Stepania,9.2 361 | George Hill,9.15 362 | Tony Battie,9.14 363 | George Lynch,9.13 364 | Damon Jones,9.12 365 | Donnell Harvey,9.1 366 | Jason Caffey,9.1 367 | Wesley Matthews,9.1 368 | Gordan Giricek,9.07 369 | Howard Eisley,9.03 370 | Luther Head,9.03 371 | Beno Udrih,9.02 372 | Stromile Swift,9.0 373 | Mikki Moore,8.92 374 | Corey Brewer,8.9 375 | Jared Dudley,8.9 376 | Rick Brunson,8.9 377 | Calbert Cheaney,8.85 378 | Fabricio Oberto,8.85 379 | Brian Skinner,8.8 380 | Dorell Wright,8.8 381 | JJ Redick,8.8 382 | Juan Carlos Navarro,8.8 383 | Tyrone Nesby,8.8 384 | Bobby Jackson,8.78 385 | Flip Murray,8.75 386 | Rashad McCants,8.75 387 | Brandon Rush,8.7 388 | Marc Jackson,8.7 389 | Marreese Speights,8.7 390 | Steve Blake,8.7 391 | Anthony Peeler,8.62 392 | Othella Harrington,8.62 393 | Troy Hudson,8.62 394 | Adonal Foyle,8.6 395 | Chase Budinger,8.6 396 | Francisco Elson,8.6 397 | Sean Williams,8.6 398 | Chuck Hayes,8.55 399 | Charlie Bell,8.53 400 | CJ Miles,8.5 401 | D.J. Augustin,8.5 402 | Felipe Lopez,8.5 403 | Johnny Newman,8.5 404 | Zeljko Rebraca,8.5 405 | Austin Croshere,8.4 406 | Goran Dragic,8.4 407 | Kevin Willis,8.4 408 | Shammond Williams,8.4 409 | Shelden Williams,8.4 410 | Vitaly Potapenko,8.4 411 | Matt Carroll,8.37 412 | Devean George,8.35 413 | Ime Udoka,8.35 414 | Martell Webster,8.33 415 | Carlos Arroyo,8.3 416 | DeMar DeRozan,8.3 417 | Fred Jones,8.3 418 | Eric Williams,8.27 419 | Rasual Butler,8.26 420 | J.J. Barea,8.25 421 | Reggie Evans,8.22 422 | Chris Childs,8.2 423 | Ira Newble,8.2 424 | Kendall Gill,8.2 425 | Lucious Harris,8.2 426 | Predrag Drobnjak,8.2 427 | Ryan Bowen,8.2 428 | Bostjan Nachbar,8.17 429 | Pat Garrity,8.15 430 | Terry Porter,8.15 431 | Darius Songaila,8.14 432 | Roger Mason Jr.,8.13 433 | Glen Davis,8.1 434 | Jiri Welsch,8.1 435 | Juan Dixon,8.1 436 | Alan Henderson,8.07 437 | Moochie Norris,8.07 438 | Trenton Hassell,8.03 439 | Damien Wilkins,8.0 440 | Darrell Arthur,8.0 441 | Jared Jeffries,8.0 442 | Shawn Kemp,8.0 443 | Walt Williams,8.0 444 | Anthony Johnson,7.97 445 | Johan Petro,7.95 446 | Marquis Daniels,7.9 447 | Sarunas Jasikevicius,7.9 448 | Willie Green,7.9 449 | Brian Cook,7.8 450 | Calvin Booth,7.8 451 | Donte Greene,7.8 452 | Gary Trent,7.8 453 | JaVale McGee,7.8 454 | John Starks,7.8 455 | Shandon Anderson,7.8 456 | DeAndre Jordan,7.7 457 | Erick Strickland,7.7 458 | Steven Hunter,7.65 459 | Corie Blount,7.6 460 | Keith Bogans,7.6 461 | Malik Allen,7.6 462 | Shannon Brown,7.6 463 | Greg Buckner,7.58 464 | Adam Morrison,7.5 465 | Charlie Ward,7.5 466 | Evan Eschmeyer,7.5 467 | Gerald Green,7.5 468 | Raul Lopez,7.5 469 | Robert Pack,7.5 470 | Sebastian Telfair,7.5 471 | Eddie House,7.47 472 | Jarvis Hayes,7.45 473 | Joey Graham,7.43 474 | Olden Polynice,7.4 475 | Robert Traylor,7.4 476 | Scott Padgett,7.4 477 | Bruce Bowen,7.39 478 | Adrian Griffin,7.3 479 | Danny Manning,7.3 480 | Tony Massenburg,7.3 481 | Andrew DeClercq,7.25 482 | Devin Brown,7.25 483 | Daequan Cook,7.2 484 | Daniel Gibson,7.2 485 | Jannero Pargo,7.2 486 | Jerryd Bayless,7.2 487 | Melvin Ely,7.2 488 | Bryon Russell,7.18 489 | Jason Collins,7.17 490 | Maurice Evans,7.1 491 | James Jones,7.03 492 | Jonathan Bender,7.0 493 | Lou Amundson,7.0 494 | Thabo Sefolosha,6.95 495 | Milt Palacio,6.93 496 | Marcin Gortat,6.9 497 | Tony Allen,6.9 498 | Keyon Dooling,6.85 499 | Luke Walton,6.85 500 | Jordan Farmar,6.83 501 | Danny Ferry,6.8 502 | DerMarr Johnson,6.8 503 | Nicolas Batum,6.8 504 | Junior Harrington,6.7 505 | Kris Humphries,6.7 506 | Rodney White,6.65 507 | Ryan Hollins,6.6 508 | Sean Rooks,6.6 509 | Walter McCarty,6.6 510 | Marcus Williams,6.5 511 | Mike Batiste,6.5 512 | Brian Shaw,6.45 513 | Brevin Knight,6.4 514 | Jerome James,6.4 515 | Ron Mercer,6.4 516 | DeSagana Diop,6.36 517 | Eric Maynor,6.3 518 | Jake Voskuhl,6.3 519 | Jelani McCoy,6.3 520 | Kevin Ollie,6.3 521 | Lindsey Hunter,6.3 522 | Sam Young,6.3 523 | Steve Novak,6.3 524 | Jason Kapono,6.27 525 | Nick Young,6.23 526 | A.C. Green,6.2 527 | Dahntay Jones,6.15 528 | Arron Afflalo,6.13 529 | Quinton Ross,6.07 530 | Jacque Vaughn,6.04 531 | Jarron Collins,6.02 532 | Sasha Vujacic,5.98 533 | Hilton Armstrong,5.9 534 | Michael Doleac,5.85 535 | Joel Anthony,5.7 536 | Marcus Banks,5.7 537 | Jason Smith,5.6 538 | John Amaechi,5.6 539 | Wayne Ellington,5.6 540 | Casey Jacobsen,5.53 541 | Monty Williams,5.5 542 | Dean Garrett,5.3 543 | Mickael Gelabale,5.3 544 | Dell Curry,5.1 545 | Michael Ruffin,5.1 546 | Rodney Carney,5.1 547 | Stacey Augmon,5.05 548 | Mateen Cleaves,5.0 549 | Jason Collier,4.9 550 | Slava Medvedenko,4.9 551 | Kareem Rush,4.83 552 | Hanno Mottola,4.8 553 | Mark Madsen,4.55 554 | Sergio Rodriguez,4.45 555 | Stephen Graham,4.3 556 | Darvin Ham,4.25 557 | Orien Greene,4.2 558 | Michael Curry,4.17 559 | Eddie Gill,4.1 560 | Jeryl Sasser,4.1 561 | Royal Ivey,4.1 562 | Roko Ukic,4.0 563 | Jeff Teague,3.9 564 | Tierre Brown,3.8 565 | Justin Reed,3.7 566 | Sasha Pavlovic,3.65 567 | DeMarre Carroll,3.6 568 | Quincy Douby,3.6 569 | Steve Kerr,3.6 570 | Brian Scalabrine,3.3 571 | Nikoloz Tskitishvili,3.3 572 | Sam Mitchell,3.15 573 | -------------------------------------------------------------------------------- /challenge_2/submission.csv: -------------------------------------------------------------------------------- 1 | author 2 | Olivia Bennett 3 | Olivia Bennett 4 | Liam Carter 5 | Liam Carter 6 | Olivia Bennett 7 | Olivia Bennett 8 | Ava Thompson 9 | Liam Carter 10 | Liam Carter 11 | Ava Thompson 12 | Liam Carter 13 | Liam Carter 14 | Mason Reed 15 | Ava Thompson 16 | Liam Carter 17 | Mason Reed 18 | Ava Thompson 19 | Mason Reed 20 | Olivia Bennett 21 | Ava Thompson 22 | Liam Carter 23 | Olivia Bennett 24 | Ava Thompson 25 | Ethan Brooks 26 | Liam Carter 27 | Liam Carter 28 | Liam Carter 29 | Olivia Bennett 30 | Liam Carter 31 | Mason Reed 32 | Olivia Bennett 33 | Mason Reed 34 | Olivia Bennett 35 | Olivia Bennett 36 | Olivia Bennett 37 | Ethan Brooks 38 | Mason Reed 39 | Liam Carter 40 | Liam Carter 41 | Olivia Bennett 42 | Liam Carter 43 | Liam Carter 44 | Olivia Bennett 45 | Liam Carter 46 | Liam Carter 47 | Ethan Brooks 48 | Mason Reed 49 | Ava Thompson 50 | Ava Thompson 51 | Liam Carter 52 | Ethan Brooks 53 | Liam Carter 54 | Liam Carter 55 | Olivia Bennett 56 | Mason Reed 57 | Liam Carter 58 | Liam Carter 59 | Ava Thompson 60 | Ava Thompson 61 | Ava Thompson 62 | Ethan Brooks 63 | Liam Carter 64 | Liam Carter 65 | Mason Reed 66 | Ava Thompson 67 | Ethan Brooks 68 | Ethan Brooks 69 | Liam Carter 70 | Ava Thompson 71 | Olivia Bennett 72 | Liam Carter 73 | Olivia Bennett 74 | Mason Reed 75 | Olivia Bennett 76 | Liam Carter 77 | Liam Carter 78 | Liam Carter 79 | Liam Carter 80 | Ethan Brooks 81 | Ava Thompson 82 | Ava Thompson 83 | Mason Reed 84 | Liam Carter 85 | Olivia Bennett 86 | Olivia Bennett 87 | Liam Carter 88 | Ethan Brooks 89 | Liam Carter 90 | Liam Carter 91 | Ethan Brooks 92 | Mason Reed 93 | Ethan Brooks 94 | Ethan Brooks 95 | Olivia Bennett 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0,0,0,0,1,0,1,0,1 682 | 1,0,0,1,0,0,0,0,1 683 | 0,0,1,0,0,0,1,0,0 684 | 0,0,0,0,0,0,1,0,1 685 | 0,0,0,0,0,1,0,0,1 686 | 0,0,1,0,0,0,1,0,1 687 | 0,0,1,0,0,0,1,0,1 688 | 0,0,1,0,0,0,0,1,0 689 | 0,0,0,0,0,0,1,0,1 690 | 0,0,1,1,0,0,0,0,1 691 | 0,1,0,0,0,0,0,0,1 692 | 0,0,1,0,0,0,1,0,1 693 | 0,1,0,0,0,0,0,1,1 694 | 0,0,1,0,0,0,1,0,1 695 | 0,0,0,0,0,0,0,0,1 696 | 0,0,0,0,0,0,0,0,1 697 | 0,0,0,0,0,0,0,0,1 698 | 0,0,1,1,0,0,0,0,1 699 | 0,0,1,1,0,0,0,0,1 700 | 0,0,0,0,0,0,0,0,0 701 | 0,1,0,0,0,1,0,0,1 702 | 0,0,1,0,0,0,1,0,1 703 | 0,0,1,0,0,0,0,1,0 704 | 0,0,0,0,0,0,0,1,1 705 | 0,1,0,0,0,1,0,0,1 706 | 0,0,0,0,0,0,1,0,1 707 | 0,0,0,0,1,0,1,0,1 708 | 0,1,0,0,0,0,1,0,1 709 | 0,1,0,0,0,1,0,1,1 710 | 0,0,0,0,0,0,1,0,1 711 | 0,0,0,0,0,0,0,0,1 712 | 0,1,0,0,0,0,0,0,1 713 | 0,1,0,0,1,0,1,0,1 714 | 0,1,0,0,0,0,0,1,1 715 | 0,0,0,0,0,0,1,0,1 716 | 0,0,0,1,0,0,1,1,1 717 | 1,0,0,0,0,0,0,0,1 718 | 0,0,0,0,0,0,1,1,1 719 | 0,1,0,0,0,1,0,0,1 720 | 0,0,0,0,0,0,0,0,0 721 | 0,0,0,0,0,0,0,0,0 722 | 1,0,0,0,0,0,0,1,1 723 | 1,1,0,1,0,0,0,1,1 724 | 0,0,0,0,0,0,0,0,0 725 | 0,0,0,0,0,0,0,1,1 726 | 0,1,0,0,0,1,0,0,1 727 | 0,0,1,0,0,0,1,0,1 728 | 0,0,0,0,1,0,1,1,1 729 | 0,1,1,0,0,0,0,0,0 730 | 0,0,1,0,0,0,1,0,1 731 | 1,0,0,1,0,1,0,0,1 732 | 0,1,0,0,0,0,0,1,1 733 | 1,0,0,1,1,0,0,0,1 734 | 0,0,0,0,1,0,1,0,1 735 | 0,0,1,0,0,0,1,0,1 736 | 0,0,1,0,0,0,0,0,0 737 | 0,0,0,0,0,0,1,0,1 738 | 0,0,0,0,0,0,0,0,1 739 | 0,0,1,0,0,0,1,0,1 740 | 1,0,0,1,0,0,0,0,1 741 | -------------------------------------------------------------------------------- /challenge_3/Student_GPA.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "

\n", 8 | "\n", 9 | "تشخیص معدل دانش آموزان\n", 10 | "

\n", 11 | "\n", 12 | "

\n", 13 | "\n", 14 | "در این سوال قصد داریم با استفاده از یک مجموعه‌داده که شامل اطلاعات دانش‌آموزان است، معدل آن‌ها را تخمین بزنیم. برای این‌کار شما باید پس از پیش‌پردازش داده‌ها، به مهندسی ویژگی و ساخت مدل مناسب بپردازید. توجه داشته باشید، در انتها، تنها مدل شما مورد بررسی قرار خواهد گرفت، اما مسلماً هرچه پیش‌پردازش و مهندسی ویژگی بهتری داشته باشید در نهایت به مدل بهتری خواهید رسید.\n", 15 | "\n", 16 | "\n", 17 | "

" 18 | ] 19 | }, 20 | { 21 | "cell_type": "markdown", 22 | "metadata": {}, 23 | "source": [ 24 | "

\n", 25 | "\n", 26 | "معرفی مجموعه داده\n", 27 | "\n", 28 | "

\n", 29 | "\n", 30 | "

\n", 31 | "\n", 32 | " در فایل اولیه‌ی این سوال یک پوشه با نام data قرار دارد.\n", 33 | " این پوشه شامل دو فایل با نام‌های train.csv و test.csv است که به‌ترتیب مجموعه‌داده‌ی آموزش و آزمون هستند.\n", 34 | " مجموعه‌داده‌ی آموزش این سوال شامل ۱۹۱۳ سطر و ۱۴ ستون است و\n", 35 | " مجموعه‌داده‌ی آزمون دارای ۴۷۹ سطر است و تنها ستون GPA را ندارد.\n", 36 | " توضیحات مربوط به ستون‌ها به شرح زیر است:\n", 37 | "\n", 38 | "

\n", 39 | "\n", 40 | "
\n", 41 | "
\n", 42 | "\n", 43 | "\n", 44 | "| نام ویژگی | توضیح ویژگی |\n", 45 | "| :---: | :---: |\n", 46 | "| StudentID | شماره‌ی دانش‌آموزی|\n", 47 | "| Age | سن فرد|\n", 48 | "| Gender | جنسیت، ۰ برای آقایان و ۱ برای خانم‌ها|\n", 49 | "| Ethnicity |قومیت دانش‌آموزان|\n", 50 | "| ParentalEducation |سطح تصحیلات خانواده‌ی دانش‌آموز|\n", 51 | "| StudyTimeWeekly | ساعت مطالعه‌ی هفتگی از ۰ تا ۲۰ ساعت|\n", 52 | "| Absences | تعداد غیبت دانش‌آموز در یک سال تحصیلی از ۰ تا ۳۰|\n", 53 | "| Tutoring | وضعیت تدریس خصوصی، ۰ نشان‌دهنده‌ی خیر و ۱ نشان‌دهنده بله|\n", 54 | "| ParentalSupport | میزان حمایت والدین از دانش‌آموز|\n", 55 | "| Extracurricular | شرکت در برنامه‌های فوق‌برنامه، ۰ نشان‌دهنده‌ی خیر و ۱ نشان‌دهنده‌ی بله|\n", 56 | "| Sports | شرکت در برنامه‌های ورزشی، ۰ نشان‌دهنده‌ی خیر و ۱ نشان‌دهنده‌ی بله||\n", 57 | "| Music | شرکت در برنامه‌های موسیقی، ۰ نشان‌دهنده‌ی خیر و ۱ نشان‌دهنده‌ی بله||\n", 58 | "| Volunteering | شرکت در برنامه‌های داوطلبانه، ۰ نشان‌دهنده‌ی خیر و ۱ نشان‌دهنده‌ی بله||\n", 59 | "| GPA | میانگین نمره در بازه‌ی صفر تا چهار|\n", 60 | "\n", 61 | "\n", 62 | "\n", 63 | "\n", 64 | "

\n", 65 | "توجه:\n", 66 | "\n", 67 | "داده‌های ارزیابی ممکن است داده‌ی گم‌شده (Nan) داشته باشند.\n", 68 | "\n", 69 | "

" 70 | ] 71 | }, 72 | { 73 | "cell_type": "markdown", 74 | "metadata": {}, 75 | "source": [ 76 | "

\n", 77 | "\n", 78 | "خواندن مجموعه داده\n", 79 | "\n", 80 | "

\n", 81 | "\n", 82 | "

\n", 83 | "\n", 84 | " در ابتدا نیاز است فایل‌های مجموعه‌داده را بخوانید. نمونه‌های آموزشی در فایل train.csv و نمونه‌های آزمون که باید مقدار متغیر هدف آن‌ها را پیش‌بینی کنید در فایل test.csv ذخیره شده‌اند. اگر لازم دانستید می‌توانید به دلخواه خود بخشی از مجموعه‌ی آموزش را به عنوان مجموعه‌ی اعتبارسنجی نیز جدا کنید.\n", 85 | "\n", 86 | "

" 87 | ] 88 | }, 89 | { 90 | "cell_type": "code", 91 | "execution_count": 58, 92 | "metadata": { 93 | "vscode": { 94 | "languageId": "plaintext" 95 | } 96 | }, 97 | "outputs": [], 98 | "source": [ 99 | "import pandas as pd\n", 100 | "import numpy as np" 101 | ] 102 | }, 103 | { 104 | "cell_type": "code", 105 | "execution_count": 64, 106 | "metadata": { 107 | "vscode": { 108 | "languageId": "plaintext" 109 | } 110 | }, 111 | "outputs": [ 112 | { 113 | "data": { 114 | "text/plain": [ 115 | "(479, 13)" 116 | ] 117 | }, 118 | "execution_count": 64, 119 | "metadata": {}, 120 | "output_type": "execute_result" 121 | } 122 | ], 123 | "source": [ 124 | "train_data = pd.read_csv('data/train.csv')\n", 125 | "test_data = pd.read_csv('data/test.csv')\n", 126 | "\n", 127 | "\n", 128 | "test_data.shape" 129 | ] 130 | }, 131 | { 132 | "cell_type": "markdown", 133 | "metadata": {}, 134 | "source": [ 135 | "

\n", 136 | "\n", 137 | "پیش‌پردازش و مهندسی ویژگی\n", 138 | "\n", 139 | "

\n", 140 | "\n", 141 | "

\n", 142 | "\n", 143 | " در این سوال شما می‌توانید از هر تکنیک پیش‌پردازش/مهندسی ویژگی دلخواهتان، استفاده کنید.\n", 144 | "
\n", 145 | " تکنیک‌هایی که استفاده می‌کنید به شکل مستقیم مورد ارزیابی توسط سامانه داوری قرار نمی‌گیرند. بلکه همه آن‌ها در دقت مدل شما تاثیر خواهند گذاشت؛ بنابراین هر چه پیش‌پردازش/مهندسی ویژگی بهتری انجام دهید تا دقت مدل بهبود پیدا کند، امتیاز بیشتری از این سوال کسب خواهید کرد.\n", 146 | "\n", 147 | "\n", 148 | "

" 149 | ] 150 | }, 151 | { 152 | "cell_type": "code", 153 | "execution_count": 60, 154 | "metadata": { 155 | "scrolled": true, 156 | "vscode": { 157 | "languageId": "plaintext" 158 | } 159 | }, 160 | "outputs": [], 161 | "source": [ 162 | "# Preprocessing step\n", 163 | "\n", 164 | "\n", 165 | "from sklearn.model_selection import train_test_split\n", 166 | "from sklearn.preprocessing import LabelEncoder, StandardScaler\n", 167 | "\n", 168 | "numeric_columns = train_data.select_dtypes(include=['float64', 'int64']).columns\n", 169 | "train_data[numeric_columns] = train_data[numeric_columns].fillna(train_data[numeric_columns].mean())\n", 170 | "\n", 171 | "\n", 172 | "numeric_columns = test_data.select_dtypes(include=['float64', 'int64']).columns\n", 173 | "test_data[numeric_columns] = test_data[numeric_columns].fillna(test_data[numeric_columns].mean())\n", 174 | "\n", 175 | "\n", 176 | "\n", 177 | "label_encoders = {}\n", 178 | "categorical_features = ['Ethnicity', 'ParentalEducation', 'ParentalSupport']\n", 179 | "\n", 180 | "for feature in categorical_features:\n", 181 | " label_encoders[feature] = LabelEncoder()\n", 182 | " train_data[feature] = label_encoders[feature].fit_transform(train_data[feature].fillna('Unknown'))\n", 183 | " test_data[feature] = label_encoders[feature].transform(test_data[feature].fillna('Unknown'))\n", 184 | "\n", 185 | "\n", 186 | "scaler = StandardScaler()\n", 187 | "features_to_scale = ['Age', 'StudyTimeWeekly', 'Absences', 'ParentalSupport']\n", 188 | "train_data[features_to_scale] = scaler.fit_transform(train_data[features_to_scale])\n", 189 | "test_data[features_to_scale] = scaler.transform(test_data[features_to_scale])\n", 190 | "\n", 191 | "X = train_data.drop(columns=['StudentID', 'GPA']) \n", 192 | "y = train_data['GPA'] \n", 193 | "\n", 194 | "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)\n" 195 | ] 196 | }, 197 | { 198 | "cell_type": "markdown", 199 | "metadata": {}, 200 | "source": [ 201 | "

\n", 202 | "\n", 203 | "آموزش مدل\n", 204 | "\n", 205 | "

\n", 206 | "\n", 207 | "

\n", 208 | "\n", 209 | " حال که داده را پاکسازی کرده و احتمالا ویژگی‌هایی را به آن افزوده یا از آن حذف کرده‌اید، وقت آن است که مدلی آموزش دهید که بتواند متغیر هدف این مسئله را پیش‌بینی کند.\n", 210 | "\n", 211 | "

" 212 | ] 213 | }, 214 | { 215 | "cell_type": "code", 216 | "execution_count": 61, 217 | "metadata": { 218 | "vscode": { 219 | "languageId": "plaintext" 220 | } 221 | }, 222 | "outputs": [], 223 | "source": [ 224 | "from sklearn.linear_model import LinearRegression\n", 225 | "from sklearn.ensemble import RandomForestRegressor\n", 226 | "from sklearn.metrics import mean_squared_error, r2_score\n", 227 | "from sklearn.model_selection import GridSearchCV\n", 228 | "\n", 229 | "\n", 230 | "linear_model = LinearRegression()\n", 231 | "linear_model.fit(X_train, y_train)\n", 232 | "\n", 233 | "\n", 234 | "\n", 235 | "\n", 236 | "rf_model = RandomForestRegressor(random_state=42)\n", 237 | "\n", 238 | "param_grid = {\n", 239 | " 'n_estimators': [100, 200],\n", 240 | " 'max_depth': [None, 10, 20],\n", 241 | " 'min_samples_split': [2, 5]\n", 242 | "}\n", 243 | "\n", 244 | "grid_search = GridSearchCV(rf_model, param_grid, cv=5, scoring='neg_mean_squared_error')\n", 245 | "grid_search.fit(X_train, y_train)\n", 246 | "\n", 247 | "best_rf_model = grid_search.best_estimator_\n" 248 | ] 249 | }, 250 | { 251 | "cell_type": "markdown", 252 | "metadata": {}, 253 | "source": [ 254 | "

\n", 255 | "\n", 256 | "معیار ارزیابی\n", 257 | "\n", 258 | "

\n", 259 | "\n", 260 | "

\n", 261 | "\n", 262 | " معیاری که برای ارزیابی عملکرد مدل انتخاب کرده‌ایم، r2_score نام دارد.\n", 263 | "
\n", 264 | " این معیار، سنجه ارزیابی کیفیت مدل شماست. به عبارت بهتر در سامانه داوری هم از همین معیار برای نمره‌دهی استفاده شده است.\n", 265 | "
\n", 266 | " پیشنهاد می‌شود با توجه به این معیار، عملکرد مدل خود را بر روی مجموعه‌ی آموزش یا اعتبارسنجی ارزیابی کنید.\n", 267 | "\n", 268 | "

\n", 269 | "\n", 270 | "

\n", 271 | "توجه:\n", 272 | "\n", 273 | " برای دریافت نمره از این سوال لازم است تا دقت مدل شما از آستانه‌ی ۰.۴ بیشتر باشد.\n", 274 | " در صورتی که دقت مدل شما از ۰.۴ کمتر باشد نمره شما \n", 275 | " صفر\n", 276 | " خواهد شد و در غیر این صورت با فرمول زیر محاسبه می‌شود:\n", 277 | "\n", 278 | "

\n" 279 | ] 280 | }, 281 | { 282 | "cell_type": "code", 283 | "execution_count": 62, 284 | "metadata": { 285 | "vscode": { 286 | "languageId": "plaintext" 287 | } 288 | }, 289 | "outputs": [ 290 | { 291 | "name": "stdout", 292 | "output_type": "stream", 293 | "text": [ 294 | "Linear Regression - MSE: 0.07, R²: 0.92\n", 295 | "Random Forest Regression - MSE: 0.07, R²: 0.92\n" 296 | ] 297 | } 298 | ], 299 | "source": [ 300 | "# evaluate your model\n", 301 | "from sklearn.metrics import r2_score\n", 302 | "\n", 303 | "y_val_pred_linear = linear_model.predict(X_val)\n", 304 | "mse_linear = mean_squared_error(y_val, y_val_pred_linear)\n", 305 | "r2_linear = r2_score(y_val, y_val_pred_linear)\n", 306 | "print(f\"Linear Regression - MSE: {mse_linear:.2f}, R²: {r2_linear:.2f}\")\n", 307 | "\n", 308 | "\n", 309 | "y_val_pred_rf = best_rf_model.predict(X_val)\n", 310 | "mse_rf = mean_squared_error(y_val, y_val_pred_rf)\n", 311 | "r2_rf = r2_score(y_val, y_val_pred_rf)\n", 312 | "print(f\"Random Forest Regression - MSE: {mse_rf:.2f}, R²: {r2_rf:.2f}\")" 313 | ] 314 | }, 315 | { 316 | "cell_type": "markdown", 317 | "metadata": {}, 318 | "source": [ 319 | "

\n", 320 | "\n", 321 | " پیش‌بینی بر روی داده‌ی تست و خروجی\n", 322 | "\n", 323 | "

\n", 324 | "\n", 325 | "

\n", 326 | "\n", 327 | " پیش‌بینی مدل خود بر روی داده‌های آزمون را در یک دیتافریم (dataframe) به فرمت زیر ذخیره کنید.\n", 328 | "\n", 329 | "

\n", 330 | "\n", 331 | "\n", 332 | "

\n", 333 | "\n", 334 | " توجه داشته باشید که نام دیتافریم باید submission باشد؛ در غیر این‌صورت، سامانه‌ی داوری قادر به ارزیابی خروجی شما نخواهد بود.\n", 335 | " این دیتافریم تنها شامل ۱ ستون با اسم GPA است و ۴۷۹ سطر دارد.\n", 336 | "
\n", 337 | " به ازای هر سطر موجود در مجموعه‌داده‌ی آزمون، باید یک مقدار پیش‌بینی‌شده داشته باشید.\n", 338 | " به‌عنوان مثال جدول زیر، ۵ سطر ابتدایی دیتافریم submission را نشان می‌دهد. البته این اعداد به‌صورت فرضی هستند و در جواب شما، اعداد ستون GPA ممکن است متفاوت باشند.\n", 339 | "\n", 340 | "

\n", 341 | "\n", 342 | "
\n", 343 | "
\n", 344 | "\n", 345 | " \n", 346 | "||GPA|\n", 347 | "|:----:|:-----:|\n", 348 | "|0|2.6765|\n", 349 | "|1|3.9865434|\n", 350 | "|2|1.0323434|\n", 351 | "|3|0.0434253|\n", 352 | "|4|2.060680|\n", 353 | "\n", 354 | "\n", 355 | "
\n", 356 | "
" 357 | ] 358 | }, 359 | { 360 | "cell_type": "code", 361 | "execution_count": 40, 362 | "metadata": { 363 | "vscode": { 364 | "languageId": "plaintext" 365 | } 366 | }, 367 | "outputs": [], 368 | "source": [ 369 | "test_predictions = linear_model.predict(test_data.drop(columns=['StudentID']))\n", 370 | "\n", 371 | "submission = pd.DataFrame({'GPA': test_predictions})\n", 372 | "\n" 373 | ] 374 | }, 375 | { 376 | "cell_type": "markdown", 377 | "metadata": {}, 378 | "source": [ 379 | "

\n", 380 | "\n", 381 | "سلول جواب‌ساز\n", 382 | "\n", 383 | "

\n", 384 | "\n", 385 | "

\n", 386 | "\n", 387 | " برای ساخته‌شدن فایل result.zip سلول زیر را اجرا کنید. توجه داشته باشید که پیش از اجرای سلول زیر تغییرات اعمال شده در نت‌بوک را ذخیره کرده باشید (ctrl+s) در غیر این صورت، در پایان مسابقه نمره شما به صفر تغییر خواهد کرد.\n", 388 | "
\n", 389 | " همچنین اگر از کولب برای اجرای این فایل نوت‌بوک استفاده می‌کنید، قبل از ارسال فایل result.zip، آخرین نسخه‌ی نوت‌بوک خود را دانلود کرده و داخل فایل ارسالی قرار دهید.\n", 390 | "" 391 | ] 392 | }, 393 | { 394 | "cell_type": "code", 395 | "execution_count": 41, 396 | "metadata": { 397 | "vscode": { 398 | "languageId": "plaintext" 399 | } 400 | }, 401 | "outputs": [ 402 | { 403 | "name": "stdout", 404 | "output_type": "stream", 405 | "text": [ 406 | "File Paths:\n", 407 | "['Student_GPA.ipynb', 'submission.csv']\n" 408 | ] 409 | } 410 | ], 411 | "source": [ 412 | "import zipfile\n", 413 | "import os\n", 414 | "\n", 415 | "if not os.path.exists(os.path.join(os.getcwd(), 'Student_GPA.ipynb')):\n", 416 | " %notebook -e Student_GPA.ipynb\n", 417 | "\n", 418 | "def compress(file_names):\n", 419 | " print(\"File Paths:\")\n", 420 | " print(file_names)\n", 421 | " compression = zipfile.ZIP_DEFLATED\n", 422 | " with zipfile.ZipFile(\"result.zip\", mode=\"w\") as zf:\n", 423 | " for file_name in file_names:\n", 424 | " zf.write('./' + file_name, file_name, compress_type=compression)\n", 425 | "\n", 426 | "submission.to_csv('submission.csv', index=False)\n", 427 | "\n", 428 | "file_names = ['Student_GPA.ipynb', 'submission.csv']\n", 429 | "compress(file_names)" 430 | ] 431 | }, 432 | { 433 | "cell_type": "code", 434 | "execution_count": null, 435 | "metadata": {}, 436 | "outputs": [], 437 | "source": [] 438 | } 439 | ], 440 | "metadata": { 441 | "kernelspec": { 442 | "display_name": "Python 3 (ipykernel)", 443 | "language": "python", 444 | "name": "python3" 445 | }, 446 | "language_info": { 447 | "codemirror_mode": { 448 | "name": "ipython", 449 | "version": 3 450 | }, 451 | "file_extension": ".py", 452 | "mimetype": "text/x-python", 453 | "name": "python", 454 | "nbconvert_exporter": "python", 455 | "pygments_lexer": "ipython3", 456 | "version": "3.11.4" 457 | } 458 | }, 459 | "nbformat": 4, 460 | "nbformat_minor": 2 461 | } 462 | -------------------------------------------------------------------------------- /challenge_1/NBA.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "id": "a708200d", 6 | "metadata": {}, 7 | "source": [ 8 | "

\n", 9 | "\n", 10 | " بسکتبالیست‌ها\n", 11 | "\n", 12 | "

" 13 | ] 14 | }, 15 | { 16 | "cell_type": "markdown", 17 | "id": "bd684f23", 18 | "metadata": {}, 19 | "source": [ 20 | "

\n", 21 | " \n", 22 | " در این سوال قصد داریم تا داده‌های مربوط به عملکرد بازیکنان لیگ بسکتبال NBA را تحلیل کنیم.\n", 23 | " پس کافی است تا کار‌هایی که از شما خواسته شده را مرحله به مرحله انجام دهید.\n", 24 | " \n", 25 | "

" 26 | ] 27 | }, 28 | { 29 | "cell_type": "markdown", 30 | "id": "8c60fd2f", 31 | "metadata": {}, 32 | "source": [ 33 | "

\n", 34 | " \n", 35 | " مجموعه‌داده\n", 36 | " \n", 37 | "

\n", 38 | "\n", 39 | "

\n", 40 | " \n", 41 | " مجموعه‌داده‌ای که در احتیار شما قرار گرفته است شامل اطلاعات عملکرد بازیکنان NBA در فصل‌های ۲۰۰۰ الی ۲۰۰۹ است.\n", 42 | " این مجموعه شامل ۱۸۳۰ سطر و ۱۸ ستون می‌باشد که هر سطر آن شامل اطلاعات یک بازیکن در یک فصل است. توضیحات مربوط به ستون‌ها نیز در جدول زیر آمده است.\n", 43 | " \n", 44 | "

\n", 45 | "\n", 46 | "
\n", 47 | "
\n", 48 | "\n", 49 | " \n", 50 | "|ستون|توضیحات|\n", 51 | "|:------:|:---:|\n", 52 | "|PlAYER|نام بازیکن|\n", 53 | "|TEAM|مخفف اسم تیم بازیکن در آن فصل|\n", 54 | "|YEAR|سال (فصل مسابقات)|\n", 55 | "|GP|تعداد بازی‌های انجام شده|\n", 56 | "|MIN|میانگین دقایق بازی کرده|\n", 57 | "|PTS|میانگین امتیاز|\n", 58 | "|FGM|میانگین تعداد پرتاب‌های ۲ امتیازی موفق|\n", 59 | "|FGA|میانگین تعداد کل پرتاب‌های ۲ امتیازی|\n", 60 | "|3PM|میانگین تعداد پرتاب‌های ۳ امتیازی موفق|\n", 61 | "|3PA|میانگین تعداد کل پرتاب‌های ۳ امتیازی|\n", 62 | "|FTM|میانگین تعداد پرتاب‌های آزاد (پنالتی) موفق|\n", 63 | "|FTA|میانگین تعداد کل پرتاب‌های آزاد (پنالتی)|\n", 64 | "|OREB|میانگین تعداد ریباند‌ها در حمله|\n", 65 | "|DREB|میانگین تعداد ریباند‌ها در دفاع|\n", 66 | "|AST|میانگین تعداد پاس‌های منجر به امتیاز|\n", 67 | "|STL|میانگین تعداد دفعات توپ‌ربایی|\n", 68 | "|BLK|میانگین تعداد بلاک‌ها|\n", 69 | "|TOV|میانگین تعداد دفعات لو دادن توپ|\n", 70 | " \n", 71 | "\n", 72 | "
\n", 73 | "
" 74 | ] 75 | }, 76 | { 77 | "cell_type": "markdown", 78 | "id": "59ec7fa8", 79 | "metadata": {}, 80 | "source": [ 81 | "

\n", 82 | "\n", 83 | " با استفاده از سلول زیر کتابخانه‌های مورد نیاز خود را import کنید و سپس مجموعه‌داده را که در فایلی به نام NBA2000-2009.csv قرار دارد، بخوانید. \n", 84 | "\n", 85 | "

" 86 | ] 87 | }, 88 | { 89 | "cell_type": "code", 90 | "execution_count": 5, 91 | "id": "5842fbd0", 92 | "metadata": {}, 93 | "outputs": [ 94 | { 95 | "data": { 96 | "text/html": [ 97 | "
\n", 98 | "\n", 111 | "\n", 112 | " \n", 113 | " \n", 114 | " \n", 115 | " \n", 116 | " \n", 117 | " \n", 118 | " \n", 119 | " \n", 120 | " \n", 121 | " \n", 122 | " \n", 123 | " \n", 124 | " \n", 125 | " \n", 126 | " \n", 127 | " \n", 128 | " \n", 129 | " \n", 130 | " \n", 131 | " \n", 132 | " \n", 133 | " \n", 134 | " \n", 135 | " \n", 136 | " \n", 137 | " \n", 138 | " \n", 139 | " \n", 140 | " \n", 141 | " \n", 142 | " \n", 143 | " \n", 144 | " \n", 145 | " \n", 146 | " \n", 147 | " \n", 148 | " \n", 149 | " \n", 150 | " \n", 151 | " \n", 152 | " \n", 153 | " \n", 154 | " \n", 155 | " \n", 156 | " \n", 157 | " \n", 158 | " \n", 159 | " \n", 160 | " \n", 161 | " \n", 162 | " \n", 163 | " \n", 164 | " \n", 165 | " \n", 166 | " \n", 167 | " \n", 168 | " \n", 169 | " \n", 170 | " \n", 171 | " \n", 172 | " \n", 173 | " \n", 174 | " \n", 175 | " \n", 176 | " \n", 177 | " \n", 178 | " \n", 179 | " \n", 180 | " \n", 181 | " \n", 182 | " \n", 183 | " \n", 184 | " \n", 185 | " \n", 186 | " \n", 187 | " \n", 188 | " \n", 189 | " \n", 190 | " \n", 191 | " \n", 192 | " \n", 193 | " \n", 194 | " \n", 195 | " \n", 196 | " \n", 197 | " \n", 198 | " \n", 199 | " \n", 200 | " \n", 201 | " \n", 202 | " \n", 203 | " \n", 204 | " \n", 205 | " \n", 206 | " \n", 207 | " \n", 208 | " \n", 209 | " \n", 210 | " \n", 211 | " \n", 212 | " \n", 213 | " \n", 214 | " \n", 215 | " \n", 216 | " \n", 217 | " \n", 218 | " \n", 219 | " \n", 220 | " \n", 221 | " \n", 222 | " \n", 223 | " \n", 224 | " \n", 225 | " \n", 226 | " \n", 227 | " \n", 228 | " \n", 229 | " \n", 230 | " \n", 231 | " \n", 232 | " \n", 233 | " \n", 234 | " \n", 235 | " \n", 236 | " \n", 237 | " \n", 238 | " \n", 239 | " \n", 240 | " \n", 241 | " \n", 242 | "
PLAYERTEAMYEARGPMINPTSFGMFGA3PM3PAFTMFTAOREBDREBASTSTLBLKTOV
0Allen IversonPHI20007142.031.110.725.51.44.38.210.10.73.14.62.50.33.3
1Jerry StackhouseDET20008040.229.89.724.12.15.98.310.11.22.75.11.20.74.1
2Shaquille O'NealLAL20007439.528.711.019.20.00.06.713.13.98.83.70.62.82.9
3Kobe BryantLAL20006840.928.510.322.20.92.97.08.21.54.35.01.70.63.2
4Vince CarterTOR20007539.727.610.222.12.25.35.16.72.33.23.91.51.12.2
\n", 243 | "
" 244 | ], 245 | "text/plain": [ 246 | " PLAYER TEAM YEAR GP MIN PTS FGM FGA 3PM 3PA FTM \\\n", 247 | "0 Allen Iverson PHI 2000 71 42.0 31.1 10.7 25.5 1.4 4.3 8.2 \n", 248 | "1 Jerry Stackhouse DET 2000 80 40.2 29.8 9.7 24.1 2.1 5.9 8.3 \n", 249 | "2 Shaquille O'Neal LAL 2000 74 39.5 28.7 11.0 19.2 0.0 0.0 6.7 \n", 250 | "3 Kobe Bryant LAL 2000 68 40.9 28.5 10.3 22.2 0.9 2.9 7.0 \n", 251 | "4 Vince Carter TOR 2000 75 39.7 27.6 10.2 22.1 2.2 5.3 5.1 \n", 252 | "\n", 253 | " FTA OREB DREB AST STL BLK TOV \n", 254 | "0 10.1 0.7 3.1 4.6 2.5 0.3 3.3 \n", 255 | "1 10.1 1.2 2.7 5.1 1.2 0.7 4.1 \n", 256 | "2 13.1 3.9 8.8 3.7 0.6 2.8 2.9 \n", 257 | "3 8.2 1.5 4.3 5.0 1.7 0.6 3.2 \n", 258 | "4 6.7 2.3 3.2 3.9 1.5 1.1 2.2 " 259 | ] 260 | }, 261 | "execution_count": 5, 262 | "metadata": {}, 263 | "output_type": "execute_result" 264 | } 265 | ], 266 | "source": [ 267 | "import pandas as pd\n", 268 | "\n", 269 | "df = pd.read_csv(\"/home/reza/Downloads/NBA/Data/NBA2000-2009.csv\")\n", 270 | "df.head()" 271 | ] 272 | }, 273 | { 274 | "cell_type": "markdown", 275 | "id": "83f6940c", 276 | "metadata": {}, 277 | "source": [ 278 | "

\n", 279 | " \n", 280 | " قسمت اول\n", 281 | " \n", 282 | "

\n", 283 | "\n", 284 | "

\n", 285 | " \n", 286 | " لیستی از بازیکن‌ها و فصل‌ها را بدست آورید که در آن فصل، آن بازیکن در حداقل ۲ تا از ۵ ملاک اصلی آمار دو رقمی (بزرگتر مساوی ۱۰) ثبت کرده باشد. \n", 287 | "
\n", 288 | " منظور از ۵ ملاک اصلی میانگین امتیاز، میانگین تعداد ریباند (مجموع ریباند در دفاع و حمله)، میانگین تعداد پاس‌های منجر به گل، میانگین تعداد بلاک‌ها و میانگین تعداد توپ‌ربایی‌ها است.\n", 289 | " \n", 290 | "
\n", 291 | " \n", 292 | " خروجی:\n", 293 | " \n", 294 | " نتیجه‌ی درخواست را در دیتافریمی با نام double_double قرار دهید.\n", 295 | " این دیتافریم باید شامل ۷ ستون باشد که ستون‌ها از چپ به راست به ترتیب نام بازیکن، سال، میانگین امتیاز، میانگین تعداد پاس‌های منجر به گل، میانگین تعداد ریباند‌ها، میانگین تعداد بلاک‌ها و میانگین تعداد توپ‌ربایی‌ها باشند. همچنین کل دیتافریم باید به ترتیب برحسب سال و نام بازیکن به صورت صعودی مرتب باشد. دو سطر اول این دیتافریم به شکل زیر است:\n", 296 | "

\n", 297 | "\n", 298 | "
\n", 299 | "
\n", 300 | "\n", 301 | " \n", 302 | "|PLAYER|YEAR|PTS|AST|REB|STL|BLK|\n", 303 | "|:---:|:---:|:---:|:---:|:---:|:---:|:---:|\n", 304 | "|Antonio Davis|2000|13.7|1.4|10.1|0.3|1.9|\n", 305 | "|Antonio McDyess|2000|20.8|2.1|12.0|0.6|1.5|\n", 306 | " \n", 307 | "\n", 308 | "
\n", 309 | "
" 310 | ] 311 | }, 312 | { 313 | "cell_type": "code", 314 | "execution_count": 19, 315 | "id": "3e079ea3", 316 | "metadata": {}, 317 | "outputs": [ 318 | { 319 | "data": { 320 | "text/html": [ 321 | "
\n", 322 | "\n", 335 | "\n", 336 | " \n", 337 | " \n", 338 | " \n", 339 | " \n", 340 | " \n", 341 | " \n", 342 | " \n", 343 | " \n", 344 | " \n", 345 | " \n", 346 | " \n", 347 | " \n", 348 | " \n", 349 | " \n", 350 | " \n", 351 | " \n", 352 | " \n", 353 | " \n", 354 | " \n", 355 | " \n", 356 | " \n", 357 | " \n", 358 | " \n", 359 | " \n", 360 | " \n", 361 | " \n", 362 | " \n", 363 | " \n", 364 | " \n", 365 | " \n", 366 | " \n", 367 | " \n", 368 | " \n", 369 | " \n", 370 | " \n", 371 | " \n", 372 | " \n", 373 | " \n", 374 | " \n", 375 | " \n", 376 | " \n", 377 | " \n", 378 | " \n", 379 | " \n", 380 | " \n", 381 | " \n", 382 | " \n", 383 | " \n", 384 | " \n", 385 | " \n", 386 | " \n", 387 | " \n", 388 | " \n", 389 | " \n", 390 | " \n", 391 | " \n", 392 | " \n", 393 | " \n", 394 | " \n", 395 | " \n", 396 | " \n", 397 | " \n", 398 | " \n", 399 | " \n", 400 | " \n", 401 | " \n", 402 | " \n", 403 | " \n", 404 | " \n", 405 | " \n", 406 | " \n", 407 | " \n", 408 | " \n", 409 | " \n", 410 | " \n", 411 | " \n", 412 | " \n", 413 | " \n", 414 | " \n", 415 | " \n", 416 | " \n", 417 | " \n", 418 | " \n", 419 | " \n", 420 | " \n", 421 | " \n", 422 | " \n", 423 | " \n", 424 | " \n", 425 | " \n", 426 | " \n", 427 | " \n", 428 | " \n", 429 | " \n", 430 | " \n", 431 | " \n", 432 | " \n", 433 | " \n", 434 | " \n", 435 | " \n", 436 | " \n", 437 | " \n", 438 | " \n", 439 | " \n", 440 | " \n", 441 | " \n", 442 | " \n", 443 | " \n", 444 | " \n", 445 | " \n", 446 | " \n", 447 | " \n", 448 | " \n", 449 | " \n", 450 | " \n", 451 | " \n", 452 | " \n", 453 | " \n", 454 | " \n", 455 | " \n", 456 | " \n", 457 | " \n", 458 | " \n", 459 | " \n", 460 | "
PLAYERYEARPTSASTREBBLKSTL
55Antonio Davis200013.71.410.11.90.3
19Antonio McDyess200020.82.112.01.50.6
5Chris Webber200027.14.211.11.71.3
92Dikembe Mutombo200010.01.013.52.70.4
23Elton Brand200020.13.210.11.61.0
........................
1670Gerald Wallace200918.22.110.11.11.5
1686Steve Nash200916.511.03.30.10.5
1672Tim Duncan200917.93.210.11.50.6
1701Troy Murphy200914.62.110.20.51.0
1655Zach Randolph200920.81.811.80.41.0
\n", 461 | "

88 rows × 7 columns

\n", 462 | "
" 463 | ], 464 | "text/plain": [ 465 | " PLAYER YEAR PTS AST REB BLK STL\n", 466 | "55 Antonio Davis 2000 13.7 1.4 10.1 1.9 0.3\n", 467 | "19 Antonio McDyess 2000 20.8 2.1 12.0 1.5 0.6\n", 468 | "5 Chris Webber 2000 27.1 4.2 11.1 1.7 1.3\n", 469 | "92 Dikembe Mutombo 2000 10.0 1.0 13.5 2.7 0.4\n", 470 | "23 Elton Brand 2000 20.1 3.2 10.1 1.6 1.0\n", 471 | "... ... ... ... ... ... ... ...\n", 472 | "1670 Gerald Wallace 2009 18.2 2.1 10.1 1.1 1.5\n", 473 | "1686 Steve Nash 2009 16.5 11.0 3.3 0.1 0.5\n", 474 | "1672 Tim Duncan 2009 17.9 3.2 10.1 1.5 0.6\n", 475 | "1701 Troy Murphy 2009 14.6 2.1 10.2 0.5 1.0\n", 476 | "1655 Zach Randolph 2009 20.8 1.8 11.8 0.4 1.0\n", 477 | "\n", 478 | "[88 rows x 7 columns]" 479 | ] 480 | }, 481 | "execution_count": 19, 482 | "metadata": {}, 483 | "output_type": "execute_result" 484 | } 485 | ], 486 | "source": [ 487 | "df['REB'] = df['OREB'] + df['DREB']\n", 488 | "\n", 489 | "def count_double_digit_stats(row):\n", 490 | " stats = [\n", 491 | " row['PTS'] >= 10,\n", 492 | " row['AST'] >= 10,\n", 493 | " row['REB'] >= 10,\n", 494 | " row['BLK'] >= 10,\n", 495 | " row['STL'] >= 10\n", 496 | " ]\n", 497 | " return sum(stats) >= 2\n", 498 | "\n", 499 | "double_double = df[df.apply(count_double_digit_stats, axis=1)]\n", 500 | "\n", 501 | "double_double = double_double[['PLAYER', 'YEAR', 'PTS', 'AST', 'REB', 'BLK', 'STL']]\n", 502 | "\n", 503 | "double_double = double_double.sort_values(by=['YEAR', 'PLAYER'])\n", 504 | "\n", 505 | "\n", 506 | "\n", 507 | "double_double\n", 508 | "\n" 509 | ] 510 | }, 511 | { 512 | "cell_type": "markdown", 513 | "id": "ae2261a6", 514 | "metadata": {}, 515 | "source": [ 516 | "

\n", 517 | " \n", 518 | " قسمت دوم\n", 519 | " \n", 520 | "

\n", 521 | "\n", 522 | "

\n", 523 | " \n", 524 | " باارزش‌ترین بازیکنان این ۱۰ فصل را مشخص کنید.\n", 525 | " ارزش یک بازیکن در یک فصل با استفاده از فرمول زیر بدست می‌آید:\n", 526 | " \n", 527 | " $$ (PTS + OREB + DREB + AST + STL + BLK) - (TOV + Missed FG + Missed FT) $$\n", 528 | " منظور از Missed FG و Missed FT به ترتیب میانگین تعداد پرتاب‌های ۲ امتیازی و آزاد از دست رفته است. \n", 529 | "
\n", 530 | " ارزش یک بازیکن در کل ۱۰ فصل برابر است با میانگین ارزش او در هر فصل.\n", 531 | "
\n", 532 | " \n", 533 | " خروجی:\n", 534 | " \n", 535 | " نتیجه‌ی درخواست را در دیتافریمی با نام best_player قرار دهید.\n", 536 | " این دیتافریم شامل دو ستون است که یکی نام بازیکن و دومی ارزش آن بازیکن است که تا دو رقم اعشار باید گرد شود. همچنین این دیتافریم باید بر اساس ارزش به صورت نزولی و سپس بر اساس نام بازیکن به‌صورت صعودی مرتب باشد.\n", 537 | " دو سطر اول این دیتافریم به شکل زیر است:\n", 538 | "

\n", 539 | "\n", 540 | "
\n", 541 | "
\n", 542 | "\n", 543 | " \n", 544 | "|PLAYER|VALUE|\n", 545 | "|:------:|:---:|\n", 546 | "|Kevin Garnett|29.74|\n", 547 | "|LeBron James|27.9|\n", 548 | " \n", 549 | "\n", 550 | "
\n", 551 | "
" 552 | ] 553 | }, 554 | { 555 | "cell_type": "code", 556 | "execution_count": 18, 557 | "id": "7ed18159", 558 | "metadata": {}, 559 | "outputs": [ 560 | { 561 | "data": { 562 | "text/html": [ 563 | "
\n", 564 | "\n", 577 | "\n", 578 | " \n", 579 | " \n", 580 | " \n", 581 | " \n", 582 | " \n", 583 | " \n", 584 | " \n", 585 | " \n", 586 | " \n", 587 | " \n", 588 | " \n", 589 | " \n", 590 | " \n", 591 | " \n", 592 | " \n", 593 | " \n", 594 | " \n", 595 | " \n", 596 | " \n", 597 | " \n", 598 | " \n", 599 | " \n", 600 | " \n", 601 | " \n", 602 | " \n", 603 | " \n", 604 | " \n", 605 | " \n", 606 | " \n", 607 | " \n", 608 | " \n", 609 | " \n", 610 | " \n", 611 | " \n", 612 | " \n", 613 | " \n", 614 | " \n", 615 | " \n", 616 | " \n", 617 | " \n", 618 | " \n", 619 | " \n", 620 | " \n", 621 | " \n", 622 | " \n", 623 | " \n", 624 | " \n", 625 | " \n", 626 | " \n", 627 | " \n", 628 | " \n", 629 | " \n", 630 | " \n", 631 | " \n", 632 | " \n", 633 | " \n", 634 | " \n", 635 | " \n", 636 | " \n", 637 | " \n", 638 | " \n", 639 | " \n", 640 | " \n", 641 | " \n", 642 | "
PLAYERValue
316Kevin Garnett29.74
335LeBron James27.90
98Chris Paul26.57
495Shaquille O'Neal26.55
169Dwyane Wade26.12
.........
432Quincy Douby3.60
512Steve Kerr3.60
65Brian Scalabrine3.30
415Nikoloz Tskitishvili3.30
478Sam Mitchell3.15
\n", 643 | "

571 rows × 2 columns

\n", 644 | "
" 645 | ], 646 | "text/plain": [ 647 | " PLAYER Value\n", 648 | "316 Kevin Garnett 29.74\n", 649 | "335 LeBron James 27.90\n", 650 | "98 Chris Paul 26.57\n", 651 | "495 Shaquille O'Neal 26.55\n", 652 | "169 Dwyane Wade 26.12\n", 653 | ".. ... ...\n", 654 | "432 Quincy Douby 3.60\n", 655 | "512 Steve Kerr 3.60\n", 656 | "65 Brian Scalabrine 3.30\n", 657 | "415 Nikoloz Tskitishvili 3.30\n", 658 | "478 Sam Mitchell 3.15\n", 659 | "\n", 660 | "[571 rows x 2 columns]" 661 | ] 662 | }, 663 | "execution_count": 18, 664 | "metadata": {}, 665 | "output_type": "execute_result" 666 | } 667 | ], 668 | "source": [ 669 | "df['Missed_FG'] = df['FGA'] - df['FGM']\n", 670 | "df['Missed_FT'] = df['FTA'] - df['FTM']\n", 671 | "\n", 672 | "df['Value'] = (df['PTS'] + df['OREB'] + df['DREB'] + df['AST'] + df['STL'] + df['BLK']) - (df['TOV'] + df['Missed_FG'] + df['Missed_FT'])\n", 673 | "\n", 674 | "best_player = df.groupby('PLAYER')['Value'].mean().reset_index()\n", 675 | "\n", 676 | "best_player['Value'] = best_player['Value'].round(2)\n", 677 | "\n", 678 | "best_player = best_player.sort_values(by=['Value', 'PLAYER'], ascending=[False, True])\n", 679 | "\n", 680 | "best_player\n", 681 | "\n" 682 | ] 683 | }, 684 | { 685 | "cell_type": "markdown", 686 | "id": "828b296d", 687 | "metadata": {}, 688 | "source": [ 689 | "

\n", 690 | " \n", 691 | " قسمت سوم\n", 692 | " \n", 693 | "

\n", 694 | "\n", 695 | "

\n", 696 | " \n", 697 | " در هر سال چه تیمی بیشترین میانگین امتیاز را ثبت کرده است؟\n", 698 | "
\n", 699 | " توجه داشته باشید که میانگین امتیاز یک تیم برابر است با مجموع میانگین امتیاز بازیکن‌هایش.\n", 700 | " \n", 701 | "
\n", 702 | " \n", 703 | " خروجی:\n", 704 | " \n", 705 | " نتیجه‌ی درخواست را در دیتافریمی با نام max_PTS_of_year قرار دهید.\n", 706 | " این دیتافریم باید شامل سه ستون باشد که ستون‌ها از چپ به راست به ترتیب سال، نام تیم و میانگین امتیاز است که امتیاز‌ها باید تا دو رقم اعشار گرد شوند. همچنین کل دیتافریم باید برحسب سال به صورت صعودی مرتب باشد. دو سطر اول این دیتافریم به شکل زیر است:\n", 707 | "

\n", 708 | "\n", 709 | "
\n", 710 | "
\n", 711 | "\n", 712 | " \n", 713 | "|YEAR|TEAM|PTS|\n", 714 | "|:------:|:---:|:---:|\n", 715 | "|2000|SAC|100.2|\n", 716 | "|2001|LAL|95.9|\n", 717 | " \n", 718 | "\n", 719 | "
\n", 720 | "
" 721 | ] 722 | }, 723 | { 724 | "cell_type": "code", 725 | "execution_count": 17, 726 | "id": "50a6c2c3", 727 | "metadata": {}, 728 | "outputs": [ 729 | { 730 | "data": { 731 | "text/html": [ 732 | "
\n", 733 | "\n", 746 | "\n", 747 | " \n", 748 | " \n", 749 | " \n", 750 | " \n", 751 | " \n", 752 | " \n", 753 | " \n", 754 | " \n", 755 | " \n", 756 | " \n", 757 | " \n", 758 | " \n", 759 | " \n", 760 | " \n", 761 | " \n", 762 | " \n", 763 | " \n", 764 | " \n", 765 | " \n", 766 | " \n", 767 | " \n", 768 | " \n", 769 | " \n", 770 | " \n", 771 | " \n", 772 | " \n", 773 | " \n", 774 | " \n", 775 | " \n", 776 | " \n", 777 | " \n", 778 | " \n", 779 | " \n", 780 | " \n", 781 | " \n", 782 | " \n", 783 | " \n", 784 | " \n", 785 | " \n", 786 | " \n", 787 | " \n", 788 | " \n", 789 | " \n", 790 | " \n", 791 | " \n", 792 | " \n", 793 | " \n", 794 | " \n", 795 | " \n", 796 | " \n", 797 | " \n", 798 | " \n", 799 | " \n", 800 | " \n", 801 | " \n", 802 | " \n", 803 | " \n", 804 | " \n", 805 | " \n", 806 | " \n", 807 | " \n", 808 | " \n", 809 | " \n", 810 | " \n", 811 | " \n", 812 | " \n", 813 | " \n", 814 | " \n", 815 | " \n", 816 | " \n", 817 | "
YEARTEAMPTS
222000SAC100.2
412001LAL95.9
772002ORL97.0
922003DEN96.0
1172004BOS106.2
1752005WAS96.8
1982006PHX105.4
2122007DEN110.7
2422008DEN97.3
2862009OKC102.5
\n", 818 | "
" 819 | ], 820 | "text/plain": [ 821 | " YEAR TEAM PTS\n", 822 | "22 2000 SAC 100.2\n", 823 | "41 2001 LAL 95.9\n", 824 | "77 2002 ORL 97.0\n", 825 | "92 2003 DEN 96.0\n", 826 | "117 2004 BOS 106.2\n", 827 | "175 2005 WAS 96.8\n", 828 | "198 2006 PHX 105.4\n", 829 | "212 2007 DEN 110.7\n", 830 | "242 2008 DEN 97.3\n", 831 | "286 2009 OKC 102.5" 832 | ] 833 | }, 834 | "execution_count": 17, 835 | "metadata": {}, 836 | "output_type": "execute_result" 837 | } 838 | ], 839 | "source": [ 840 | "team_pts_per_year = df.groupby(['YEAR', 'TEAM'])['PTS'].sum().reset_index()\n", 841 | "\n", 842 | "team_pts_per_year['PTS'] = team_pts_per_year['PTS'].round(2)\n", 843 | "\n", 844 | "max_PTS_of_year = team_pts_per_year.loc[team_pts_per_year.groupby('YEAR')['PTS'].idxmax()]\n", 845 | "\n", 846 | "max_PTS_of_year = max_PTS_of_year.sort_values(by='YEAR')\n", 847 | "\n", 848 | "max_PTS_of_year" 849 | ] 850 | }, 851 | { 852 | "cell_type": "markdown", 853 | "id": "92aa3439", 854 | "metadata": {}, 855 | "source": [ 856 | "

\n", 857 | "\n", 858 | "سلول جواب‌ساز\n", 859 | "\n", 860 | "

\n", 861 | "\n", 862 | "

\n", 863 | "\n", 864 | " برای ساخته‌شدن فایل result.zip سلول زیر را اجرا کنید. توجه داشته باشید که پیش از اجرای سلول زیر تغییرات اعمال شده در نت‌بوک را ذخیره کرده باشید (ctrl+s) در غیر این صورت، در پایان مسابقه نمره شما به صفر تغییر خواهد کرد.\n", 865 | "
\n", 866 | " همچنین اگر از کولب برای اجرای این فایل نوت‌بوک استفاده می‌کنید، قبل از ارسال فایل result.zip، آخرین نسخه‌ی نوت‌بوک خود را دانلود کرده و داخل فایل ارسالی قرار دهید.\n", 867 | "" 868 | ] 869 | }, 870 | { 871 | "cell_type": "code", 872 | "execution_count": 20, 873 | "id": "b887c08c", 874 | "metadata": {}, 875 | "outputs": [ 876 | { 877 | "ename": "NameError", 878 | "evalue": "name 'os' is not defined", 879 | "output_type": "error", 880 | "traceback": [ 881 | "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", 882 | "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", 883 | "Cell \u001b[0;32mIn [20], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mzipfile\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[43mos\u001b[49m\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mexists(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(os\u001b[38;5;241m.\u001b[39mgetcwd(), \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNBA.ipynb\u001b[39m\u001b[38;5;124m'\u001b[39m)):\n\u001b[1;32m 4\u001b[0m get_ipython()\u001b[38;5;241m.\u001b[39mrun_line_magic(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnotebook\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m-e NBA.ipynb\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 6\u001b[0m double_double\u001b[38;5;241m.\u001b[39mto_csv(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdouble_double.csv\u001b[39m\u001b[38;5;124m'\u001b[39m, index\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n", 884 | "\u001b[0;31mNameError\u001b[0m: name 'os' is not defined" 885 | ] 886 | } 887 | ], 888 | "source": [ 889 | "import zipfile\n", 890 | "import os\n", 891 | "\n", 892 | "if not os.path.exists(os.path.join(os.getcwd(), 'NBA.ipynb')):\n", 893 | " %notebook -e NBA.ipynb\n", 894 | "\n", 895 | "double_double.to_csv('double_double.csv', index=False)\n", 896 | "best_player.to_csv('best_player.csv', index=False)\n", 897 | "max_PTS_of_year.to_csv('max_PTS_of_year.csv', index=False)\n", 898 | "\n", 899 | "def compress(file_names):\n", 900 | " print(\"File Paths:\")\n", 901 | " print(file_names)\n", 902 | " compression = zipfile.ZIP_DEFLATED\n", 903 | " with zipfile.ZipFile(\"result.zip\", mode=\"w\") as zf:\n", 904 | " for file_name in file_names:\n", 905 | " zf.write('./' + file_name, file_name, compress_type=compression)\n", 906 | "\n", 907 | "file_names = ['double_double.csv', 'best_player.csv', 'max_PTS_of_year.csv', 'NBA.ipynb']\n", 908 | "compress(file_names)" 909 | ] 910 | }, 911 | { 912 | "cell_type": "code", 913 | "execution_count": null, 914 | "id": "31197d50", 915 | "metadata": {}, 916 | "outputs": [], 917 | "source": [] 918 | } 919 | ], 920 | "metadata": { 921 | "kernelspec": { 922 | "display_name": "Python 3 (ipykernel)", 923 | "language": "python", 924 | "name": "python3" 925 | }, 926 | "language_info": { 927 | "codemirror_mode": { 928 | "name": "ipython", 929 | "version": 3 930 | }, 931 | "file_extension": ".py", 932 | "mimetype": "text/x-python", 933 | "name": "python", 934 | "nbconvert_exporter": "python", 935 | "pygments_lexer": "ipython3", 936 | "version": "3.11.4" 937 | } 938 | }, 939 | "nbformat": 4, 940 | "nbformat_minor": 5 941 | } 942 | -------------------------------------------------------------------------------- /challenge_2/Authors.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": { 6 | "id": "Ez_7eII7F389" 7 | }, 8 | "source": [ 9 | "

\n", 10 | "\n", 11 | "دردسر سردبیر\n", 12 | "\n", 13 | "

" 14 | ] 15 | }, 16 | { 17 | "cell_type": "markdown", 18 | "metadata": { 19 | "id": "OQINW5auF5h-" 20 | }, 21 | "source": [ 22 | "

\n", 23 | "\n", 24 | "مقدمه و صورت مسئله\n", 25 | "\n", 26 | "

\n", 27 | "\n", 28 | "

\n", 29 | "\n", 30 | "هدف از این سوال توسعه‌ی یک مدل یادگیری ماشین است که قادر به پیش‌بینی دقیق نویسنده‌ی یک متن تنها بر اساس وجود یا عدم وجود کلمات خاص است.\n", 31 | "\n", 32 | "

" 33 | ] 34 | }, 35 | { 36 | "cell_type": "markdown", 37 | "metadata": { 38 | "id": "aSHQ19QVHSNh" 39 | }, 40 | "source": [ 41 | "

\n", 42 | "\n", 43 | "معرفی مجموعه داده\n", 44 | "\n", 45 | "

\n", 46 | "\n", 47 | "

\n", 48 | "\n", 49 | "یک مجموعه داده‌ی ساختاریافته به شما ارائه شده است که در آن هر ردیف نشان دهنده‌ی یک متن است و ستون ها یا وجود کلمات خاص در متن یا نویسنده‌ی متن را نشان می‌دهند.\n", 50 | "

\n", 51 | "\n", 52 | "

\n", 53 | "\n", 54 | "ستون‌های کلمات: این ستون ها بیانگر کلمات جداگانه هستند. هر ورودی در این ستون‌ها باینری است که 1 نشان دهنده‌ی وجود کلمه در متن و 0 نشان دهنده‌ی عدم وجود آن است.\n", 55 | "
\n", 56 | "ستون author: این ستون حاوی نام نویسنده‌ای است که متن را نوشته است. این ستون، متغیر هدفی است که مدل شما باید آن را پیش‌بینی کند.\n", 57 | "

\n", 58 | "\n", 59 | "
\n", 60 | "
\n", 61 | "\n", 62 | " \n", 63 | "|word_1|word_2|...|word_n|author|\n", 64 | "|:------:|:---:|:---:|:---:|:---:|\n", 65 | "|0|1|...|1|Mason Reed|\n", 66 | "|1|1|...|1|Ava Thompson|\n", 67 | "|0|1|...|0|Liam Carter|\n", 68 | "\n", 69 | "\n", 70 | "
\n", 71 | "
\n", 72 | "\n", 73 | "\n", 74 | "

\n", 75 | "\n", 76 | " مجموعه داده آزمایش نیز مانند مجموعه آموزش است با این تفاوت که ستون author که متغیر هدف مسئله است را در خود ندارد. مجموعه داده آزمایش ۲۷۶۵ سطر دارد.\n", 77 | "\n", 78 | "

\n" 79 | ] 80 | }, 81 | { 82 | "cell_type": "markdown", 83 | "metadata": {}, 84 | "source": [ 85 | "

\n", 86 | "\n", 87 | "خواندن مجموعه داده\n", 88 | "\n", 89 | "

\n", 90 | "\n", 91 | "

\n", 92 | "\n", 93 | " در ابتدا نیاز است فایل‌های مجموعه‌داده را بخوانید. نمونه‌های آموزشی در فایل train.csv و نمونه‌های آزمون که باید مقدار متغیر هدف آن‌ها را پیش‌بینی کنید در فایل test.csv ذخیره شده‌اند. اگر لازم دانستید می‌توانید به دلخواه خود بخشی از مجموعه‌ی آموزش را به عنوان مجموعه‌ی اعتبارسنجی نیز جدا کنید.\n", 94 | "\n", 95 | "

" 96 | ] 97 | }, 98 | { 99 | "cell_type": "code", 100 | "execution_count": 30, 101 | "metadata": {}, 102 | "outputs": [], 103 | "source": [ 104 | "import pandas as pd\n", 105 | "import numpy as np" 106 | ] 107 | }, 108 | { 109 | "cell_type": "code", 110 | "execution_count": 31, 111 | "metadata": {}, 112 | "outputs": [ 113 | { 114 | "data": { 115 | "text/html": [ 116 | "
\n", 117 | "\n", 130 | "\n", 131 | " \n", 132 | " \n", 133 | " \n", 134 | " \n", 135 | " \n", 136 | " \n", 137 | " \n", 138 | " \n", 139 | " \n", 140 | " \n", 141 | " \n", 142 | " \n", 143 | " \n", 144 | " \n", 145 | " \n", 146 | " \n", 147 | " \n", 148 | " \n", 149 | " \n", 150 | " \n", 151 | " \n", 152 | " \n", 153 | " \n", 154 | " \n", 155 | " \n", 156 | " \n", 157 | " \n", 158 | " \n", 159 | " \n", 160 | " \n", 161 | " \n", 162 | " \n", 163 | " \n", 164 | " \n", 165 | " \n", 166 | " \n", 167 | " \n", 168 | " \n", 169 | " \n", 170 | " \n", 171 | " \n", 172 | " \n", 173 | " \n", 174 | " \n", 175 | " \n", 176 | " \n", 177 | " \n", 178 | " \n", 179 | " \n", 180 | " \n", 181 | " \n", 182 | " \n", 183 | " \n", 184 | " \n", 185 | " \n", 186 | " \n", 187 | " \n", 188 | " \n", 189 | " \n", 190 | " \n", 191 | " \n", 192 | " \n", 193 | " \n", 194 | " \n", 195 | " \n", 196 | " \n", 197 | " \n", 198 | " \n", 199 | " \n", 200 | " \n", 201 | " \n", 202 | " \n", 203 | " \n", 204 | " \n", 205 | " \n", 206 | " \n", 207 | " \n", 208 | " \n", 209 | " \n", 210 | " \n", 211 | " \n", 212 | " \n", 213 | " \n", 214 | " \n", 215 | " \n", 216 | " \n", 217 | " \n", 218 | " \n", 219 | " \n", 220 | " \n", 221 | " \n", 222 | " \n", 223 | " \n", 224 | " \n", 225 | " \n", 226 | " \n", 227 | " \n", 228 | " \n", 229 | " \n", 230 | " \n", 231 | " \n", 232 | " \n", 233 | " \n", 234 | " \n", 235 | " \n", 236 | " \n", 237 | " \n", 238 | " \n", 239 | " \n", 240 | " \n", 241 | " \n", 242 | " \n", 243 | " \n", 244 | " \n", 245 | " \n", 246 | " \n", 247 | " \n", 248 | " \n", 249 | " \n", 250 | " \n", 251 | " \n", 252 | " \n", 253 | " \n", 254 | " \n", 255 | " \n", 256 | " \n", 257 | " \n", 258 | " \n", 259 | " \n", 260 | " \n", 261 | " \n", 262 | " \n", 263 | " \n", 264 | " \n", 265 | " \n", 266 | " \n", 267 | " \n", 268 | " \n", 269 | " \n", 270 | " \n", 271 | " \n", 272 | " \n", 273 | " \n", 274 | " \n", 275 | " \n", 276 | " \n", 277 | " \n", 278 | " \n", 279 | "
lungcouncilsolutionquiterainhairskilldifficultyaddpull...stockingnearoildivemanyruntenderasleepeatsweep
00000000000...0000000000
10000000000...0000000000
20000000000...0000000000
30000000000...0000000000
40000000000...0000000000
\n", 280 | "

5 rows × 380 columns

\n", 281 | "
" 282 | ], 283 | "text/plain": [ 284 | " lung council solution quite rain hair skill difficulty add pull \\\n", 285 | "0 0 0 0 0 0 0 0 0 0 0 \n", 286 | "1 0 0 0 0 0 0 0 0 0 0 \n", 287 | "2 0 0 0 0 0 0 0 0 0 0 \n", 288 | "3 0 0 0 0 0 0 0 0 0 0 \n", 289 | "4 0 0 0 0 0 0 0 0 0 0 \n", 290 | "\n", 291 | " ... stocking near oil dive many run tender asleep eat sweep \n", 292 | "0 ... 0 0 0 0 0 0 0 0 0 0 \n", 293 | "1 ... 0 0 0 0 0 0 0 0 0 0 \n", 294 | "2 ... 0 0 0 0 0 0 0 0 0 0 \n", 295 | "3 ... 0 0 0 0 0 0 0 0 0 0 \n", 296 | "4 ... 0 0 0 0 0 0 0 0 0 0 \n", 297 | "\n", 298 | "[5 rows x 380 columns]" 299 | ] 300 | }, 301 | "execution_count": 31, 302 | "metadata": {}, 303 | "output_type": "execute_result" 304 | } 305 | ], 306 | "source": [ 307 | "# Reading/Loading the dataset files\n", 308 | "train = pd.read_csv(\"/home/reza/Downloads/Authors/data/train.csv\")\n", 309 | "test = pd.read_csv(\"/home/reza/Downloads/Authors/data/test.csv\")\n", 310 | "\n", 311 | "test.head()" 312 | ] 313 | }, 314 | { 315 | "cell_type": "markdown", 316 | "metadata": {}, 317 | "source": [ 318 | "

\n", 319 | "\n", 320 | "پیش‌پردازش و مهندسی ویژگی\n", 321 | "\n", 322 | "

\n", 323 | "\n", 324 | "

\n", 325 | "\n", 326 | " در این سوال شما می‌توانید از هر تکنیک پیش‌پردازش/مهندسی ویژگی دلخواهتان، استفاده کنید.\n", 327 | "
\n", 328 | " تکنیک‌هایی که استفاده می‌کنید به شکل مستقیم مورد ارزیابی توسط سامانه داوری قرار نمی‌گیرند. بلکه همه آن‌ها در دقت مدل شما تاثیر خواهند گذاشت؛ بنابراین هر چه پیش‌پردازش/مهندسی ویژگی بهتری انجام دهید تا دقت مدل بهبود پیدا کند، امتیاز بیشتری از این سوال کسب خواهید کرد.\n", 329 | "\n", 330 | "\n", 331 | "

" 332 | ] 333 | }, 334 | { 335 | "cell_type": "code", 336 | "execution_count": 32, 337 | "metadata": {}, 338 | "outputs": [ 339 | { 340 | "data": { 341 | "text/plain": [ 342 | "(3196, 381)" 343 | ] 344 | }, 345 | "execution_count": 32, 346 | "metadata": {}, 347 | "output_type": "execute_result" 348 | } 349 | ], 350 | "source": [ 351 | "#pass\n", 352 | "\n", 353 | "test.shape\n", 354 | "train.shape\n" 355 | ] 356 | }, 357 | { 358 | "cell_type": "markdown", 359 | "metadata": {}, 360 | "source": [ 361 | "

\n", 362 | "\n", 363 | "آموزش مدل\n", 364 | "\n", 365 | "

\n", 366 | "\n", 367 | "

\n", 368 | "\n", 369 | " حال که داده را پاکسازی کرده و احتمالا ویژگی‌هایی را به آن افزوده یا از آن حذف کرده‌اید، وقت آن است که مدلی آموزش دهید که بتواند متغیر هدف این مسئله را پیش‌بینی کند.\n", 370 | "\n", 371 | "

" 372 | ] 373 | }, 374 | { 375 | "cell_type": "code", 376 | "execution_count": 33, 377 | "metadata": {}, 378 | "outputs": [], 379 | "source": [ 380 | "from sklearn.model_selection import train_test_split\n", 381 | "from sklearn.preprocessing import LabelEncoder\n", 382 | "from sklearn.ensemble import RandomForestClassifier\n", 383 | "from sklearn.metrics import accuracy_score\n", 384 | "from sklearn.model_selection import GridSearchCV\n", 385 | "\n", 386 | "\n", 387 | "\n", 388 | "X = train.drop(columns=['author']) \n", 389 | "y = train['author'] \n", 390 | "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)\n", 391 | "\n", 392 | "rf = RandomForestClassifier(random_state=42)\n", 393 | "\n", 394 | "param_grid = {\n", 395 | " 'n_estimators': [100, 200],\n", 396 | " 'max_depth': [10, 20, None],\n", 397 | " 'min_samples_split': [2, 5],\n", 398 | "}\n", 399 | "\n", 400 | "grid_search = GridSearchCV(rf, param_grid, cv=5, scoring='accuracy')\n", 401 | "grid_search.fit(X_train, y_train)\n", 402 | "\n", 403 | "best_model = grid_search.best_estimator_" 404 | ] 405 | }, 406 | { 407 | "cell_type": "markdown", 408 | "metadata": { 409 | "id": "yTtH1TeSJaAx" 410 | }, 411 | "source": [ 412 | "

\n", 413 | "\n", 414 | "معیار ارزیابی\n", 415 | "\n", 416 | "

\n", 417 | "\n", 418 | "

\n", 419 | "\n", 420 | " ارسال‌ها بر اساس امتیاز F1 ارزیابی می‌شوند و مدل میانگین‌گیری macro است. امتیاز F1 معیاری برای ارزیابی مدل است که هم بازیابی (Recall) و هم صحت (Accuracy) را در نظر می‌گیرد.\n", 421 | " نتیجه‌ی نهایی بر اساس فرمول زیر محاسبه می‌گردد:\n", 422 | "\n", 423 | "$$score= round(f1score, 3) \\times 100$$\n", 424 | "\n", 425 | "

\n", 426 | "

\n", 427 | "\n", 428 | "مقدار F1 Score مدل شما تا ۳ رقم اعشار گرد شده و پس از ضرب در ۱۰۰ به‌عنوان امتیاز شما از این سوال لحاظ می‌شود. بیشترین امتیاز ممکن از این سوال ۱۰۰ و کمترین امتیاز قابل قبول، ۴۰ می‌باشد. امتیاز F1 کمتر از ۴۰ معادل صفر در نظر گرفته خواهد شد.\n", 429 | "
\n", 430 | " پیشنهاد می‌شود با توجه به این معیار، عملکرد مدل خود را بر روی مجموعه‌ی آموزش یا اعتبارسنجی ارزیابی کنید.\n", 431 | "\n", 432 | "

" 433 | ] 434 | }, 435 | { 436 | "cell_type": "code", 437 | "execution_count": 34, 438 | "metadata": {}, 439 | "outputs": [ 440 | { 441 | "name": "stdout", 442 | "output_type": "stream", 443 | "text": [ 444 | "Accuracy on validation set: 0.82\n", 445 | "F1 Score on validation set: 0.82\n" 446 | ] 447 | } 448 | ], 449 | "source": [ 450 | "from sklearn.metrics import f1_score\n", 451 | "\n", 452 | "y_pred = best_model.predict(X_val)\n", 453 | "\n", 454 | "accuracy = accuracy_score(y_val, y_pred)\n", 455 | "f1 = f1_score(y_val, y_pred, average='weighted')\n", 456 | "print(f\"Accuracy on validation set: {accuracy:.2f}\")\n", 457 | "print(f\"F1 Score on validation set: {f1:.2f}\")\n" 458 | ] 459 | }, 460 | { 461 | "cell_type": "markdown", 462 | "metadata": { 463 | "id": "RGlIi8m9KtD8" 464 | }, 465 | "source": [ 466 | "

\n", 467 | "\n", 468 | " پیش‌بینی برای داده تست و خروجی\n", 469 | "\n", 470 | "

\n", 471 | "\n", 472 | "

\n", 473 | "\n", 474 | " پیش‌بینی مدل خود بر روی داده‌های آزمایش را در قالب یک dataframe ذخیره کنید. این dataframe باید دارای یک ستون با نام author باشد که ردیف iام آن، پیش‌بینی شما برای سطر iام مجموعه‌داده‌ی آزمون باشد.\n", 475 | "\n", 476 | "

\n", 477 | "\n", 478 | "
\n", 479 | "\n", 480 | " \n", 481 | "|ستون|توضیحات|\n", 482 | "|------|---|\n", 483 | "|author|نویسنده‌ی متن|\n", 484 | " \n", 485 | "\n", 486 | "
" 487 | ] 488 | }, 489 | { 490 | "cell_type": "code", 491 | "execution_count": 36, 492 | "metadata": { 493 | "id": "Q3Jx3gIdwvts" 494 | }, 495 | "outputs": [ 496 | { 497 | "data": { 498 | "text/plain": [ 499 | "array(['Olivia Bennett', 'Olivia Bennett', 'Liam Carter', 'Liam Carter',\n", 500 | " 'Olivia Bennett', 'Olivia Bennett', 'Ava Thompson', 'Liam Carter',\n", 501 | " 'Liam Carter', 'Ava Thompson', 'Liam Carter', 'Liam Carter',\n", 502 | " 'Mason Reed', 'Ava Thompson', 'Liam Carter', 'Mason Reed',\n", 503 | " 'Ava Thompson', 'Mason Reed', 'Olivia Bennett', 'Ava Thompson',\n", 504 | " 'Liam Carter', 'Olivia Bennett', 'Ava Thompson', 'Ethan Brooks',\n", 505 | " 'Liam Carter', 'Liam Carter', 'Liam Carter', 'Olivia Bennett',\n", 506 | " 'Liam Carter', 'Mason Reed', 'Olivia Bennett', 'Mason Reed',\n", 507 | " 'Olivia Bennett', 'Olivia Bennett', 'Olivia Bennett',\n", 508 | " 'Ethan Brooks', 'Mason Reed', 'Liam Carter', 'Liam Carter',\n", 509 | " 'Olivia Bennett', 'Liam Carter', 'Liam Carter', 'Olivia Bennett',\n", 510 | " 'Liam Carter', 'Liam Carter', 'Ethan Brooks', 'Mason Reed',\n", 511 | " 'Ava Thompson', 'Ava Thompson', 'Liam Carter', 'Ethan Brooks',\n", 512 | " 'Liam Carter', 'Liam Carter', 'Olivia Bennett', 'Mason Reed',\n", 513 | " 'Liam Carter', 'Liam Carter', 'Ava Thompson', 'Ava Thompson',\n", 514 | " 'Ava Thompson', 'Ethan Brooks', 'Liam Carter', 'Liam Carter',\n", 515 | " 'Mason Reed', 'Ava Thompson', 'Ethan Brooks', 'Ethan Brooks',\n", 516 | " 'Liam Carter', 'Ava Thompson', 'Olivia Bennett', 'Liam Carter',\n", 517 | " 'Olivia Bennett', 'Mason Reed', 'Olivia Bennett', 'Liam Carter',\n", 518 | " 'Liam Carter', 'Liam Carter', 'Liam Carter', 'Ethan Brooks',\n", 519 | " 'Ava Thompson', 'Ava Thompson', 'Mason Reed', 'Liam Carter',\n", 520 | " 'Olivia Bennett', 'Olivia Bennett', 'Liam Carter', 'Ethan Brooks',\n", 521 | " 'Liam Carter', 'Liam Carter', 'Ethan Brooks', 'Mason Reed',\n", 522 | " 'Ethan Brooks', 'Ethan Brooks', 'Olivia Bennett', 'Olivia Bennett',\n", 523 | " 'Liam Carter', 'Ava Thompson', 'Ethan Brooks', 'Liam Carter',\n", 524 | " 'Liam Carter', 'Ethan Brooks', 'Olivia Bennett', 'Ethan Brooks',\n", 525 | " 'Ethan Brooks', 'Olivia Bennett', 'Liam Carter', 'Ethan Brooks',\n", 526 | " 'Ethan Brooks', 'Liam Carter', 'Mason Reed', 'Olivia Bennett',\n", 527 | " 'Ava Thompson', 'Olivia Bennett', 'Ava Thompson', 'Mason Reed',\n", 528 | " 'Liam Carter', 'Ethan Brooks', 'Mason Reed', 'Liam Carter',\n", 529 | " 'Liam Carter', 'Olivia Bennett', 'Ethan Brooks', 'Olivia Bennett',\n", 530 | " 'Mason Reed', 'Mason Reed', 'Olivia Bennett', 'Liam Carter',\n", 531 | " 'Ethan Brooks', 'Ethan Brooks', 'Ava Thompson', 'Olivia Bennett',\n", 532 | " 'Ava Thompson', 'Mason Reed', 'Liam Carter', 'Olivia Bennett',\n", 533 | " 'Mason Reed', 'Olivia Bennett', 'Mason Reed', 'Olivia Bennett',\n", 534 | " 'Ethan Brooks', 'Ethan Brooks', 'Ethan Brooks', 'Ava Thompson',\n", 535 | " 'Liam Carter', 'Ava Thompson', 'Ethan Brooks', 'Mason Reed',\n", 536 | " 'Liam Carter', 'Ava Thompson', 'Olivia Bennett', 'Ava Thompson',\n", 537 | " 'Ethan Brooks', 'Ava Thompson', 'Mason Reed', 'Olivia Bennett',\n", 538 | " 'Ethan Brooks', 'Olivia Bennett', 'Ava Thompson', 'Mason Reed',\n", 539 | " 'Mason Reed', 'Liam Carter', 'Ethan Brooks', 'Olivia Bennett',\n", 540 | " 'Ava Thompson', 'Mason Reed', 'Mason Reed', 'Olivia Bennett',\n", 541 | " 'Ava Thompson', 'Ava Thompson', 'Ava Thompson', 'Ethan Brooks',\n", 542 | " 'Mason Reed', 'Liam Carter', 'Liam Carter', 'Olivia Bennett',\n", 543 | " 'Ava Thompson', 'Ava Thompson', 'Liam Carter', 'Olivia Bennett',\n", 544 | " 'Mason Reed', 'Liam Carter', 'Ava Thompson', 'Ava Thompson',\n", 545 | " 'Mason Reed', 'Ethan Brooks', 'Liam Carter', 'Olivia Bennett',\n", 546 | " 'Liam Carter', 'Liam Carter', 'Olivia Bennett', 'Mason Reed',\n", 547 | " 'Mason Reed', 'Mason Reed', 'Olivia Bennett', 'Mason Reed',\n", 548 | " 'Liam Carter', 'Ava Thompson', 'Ethan Brooks', 'Liam Carter',\n", 549 | " 'Ethan Brooks', 'Mason Reed', 'Ethan Brooks', 'Ethan Brooks',\n", 550 | " 'Ethan Brooks', 'Liam Carter', 'Olivia Bennett', 'Ava Thompson',\n", 551 | " 'Olivia Bennett', 'Mason Reed', 'Liam Carter', 'Ethan Brooks',\n", 552 | " 'Olivia Bennett', 'Ethan Brooks', 'Liam Carter', 'Liam Carter',\n", 553 | " 'Olivia Bennett', 'Liam Carter', 'Ava Thompson', 'Olivia Bennett',\n", 554 | " 'Liam Carter', 'Ethan Brooks', 'Mason Reed', 'Mason Reed',\n", 555 | " 'Mason Reed', 'Ethan Brooks', 'Ethan Brooks', 'Olivia Bennett',\n", 556 | " 'Olivia Bennett', 'Liam Carter', 'Olivia Bennett', 'Ethan Brooks',\n", 557 | " 'Mason Reed', 'Mason Reed', 'Olivia Bennett', 'Ethan Brooks',\n", 558 | " 'Mason Reed', 'Liam Carter', 'Mason Reed', 'Ava Thompson',\n", 559 | " 'Olivia Bennett', 'Mason Reed', 'Ava Thompson', 'Liam Carter',\n", 560 | " 'Ethan Brooks', 'Olivia Bennett', 'Ava Thompson', 'Liam Carter',\n", 561 | " 'Ava Thompson', 'Mason Reed', 'Liam Carter', 'Ethan Brooks',\n", 562 | " 'Mason Reed', 'Olivia Bennett', 'Ethan Brooks', 'Olivia Bennett',\n", 563 | " 'Ethan Brooks', 'Mason Reed', 'Mason Reed', 'Ava Thompson',\n", 564 | " 'Liam Carter', 'Ava Thompson', 'Ava Thompson', 'Olivia Bennett',\n", 565 | " 'Liam Carter', 'Ethan Brooks', 'Liam Carter', 'Ethan Brooks',\n", 566 | " 'Mason Reed', 'Ethan Brooks', 'Ethan Brooks', 'Olivia Bennett',\n", 567 | " 'Olivia Bennett', 'Mason Reed', 'Olivia Bennett', 'Mason Reed',\n", 568 | " 'Ethan Brooks', 'Ethan Brooks', 'Mason Reed', 'Mason Reed',\n", 569 | " 'Mason Reed', 'Mason Reed', 'Olivia Bennett', 'Mason Reed',\n", 570 | " 'Ethan Brooks', 'Ava Thompson', 'Liam Carter', 'Mason Reed',\n", 571 | " 'Ava Thompson', 'Olivia Bennett', 'Olivia Bennett', 'Ava Thompson',\n", 572 | " 'Ava Thompson', 'Liam Carter', 'Olivia Bennett', 'Liam Carter',\n", 573 | " 'Ava Thompson', 'Liam Carter', 'Mason Reed', 'Mason Reed',\n", 574 | " 'Mason Reed', 'Ava Thompson', 'Liam Carter', 'Ethan Brooks',\n", 575 | " 'Mason Reed', 'Liam Carter', 'Ethan Brooks', 'Ethan Brooks',\n", 576 | " 'Ethan Brooks', 'Liam Carter', 'Ethan Brooks', 'Mason Reed',\n", 577 | " 'Ava Thompson', 'Liam Carter', 'Ethan Brooks', 'Liam Carter',\n", 578 | " 'Ethan Brooks', 'Mason Reed', 'Liam Carter', 'Ethan Brooks',\n", 579 | " 'Ava Thompson', 'Liam Carter', 'Mason Reed', 'Ethan Brooks',\n", 580 | " 'Ava Thompson', 'Olivia Bennett', 'Mason Reed', 'Mason Reed',\n", 581 | " 'Mason Reed', 'Ethan Brooks', 'Olivia Bennett', 'Olivia Bennett',\n", 582 | " 'Ava Thompson', 'Ethan Brooks', 'Olivia Bennett', 'Ava Thompson',\n", 583 | " 'Ava Thompson', 'Liam Carter', 'Ethan Brooks', 'Olivia Bennett',\n", 584 | " 'Liam Carter', 'Liam Carter', 'Liam Carter', 'Liam Carter',\n", 585 | " 'Ethan Brooks', 'Ethan Brooks', 'Mason Reed', 'Mason Reed',\n", 586 | " 'Liam Carter', 'Olivia Bennett', 'Olivia Bennett', 'Ava Thompson',\n", 587 | " 'Ava Thompson', 'Ethan Brooks', 'Ava Thompson', 'Olivia Bennett',\n", 588 | " 'Olivia Bennett', 'Ethan Brooks', 'Olivia Bennett', 'Ava Thompson',\n", 589 | " 'Ethan Brooks', 'Mason Reed', 'Ethan Brooks', 'Olivia Bennett',\n", 590 | " 'Olivia Bennett', 'Liam Carter', 'Mason Reed', 'Olivia Bennett',\n", 591 | " 'Liam Carter', 'Mason Reed', 'Mason Reed', 'Ethan Brooks',\n", 592 | " 'Ava Thompson', 'Ava Thompson', 'Liam Carter', 'Mason Reed',\n", 593 | " 'Liam Carter', 'Ethan Brooks', 'Ethan Brooks', 'Ava Thompson',\n", 594 | " 'Mason Reed', 'Liam Carter', 'Ava Thompson', 'Liam Carter',\n", 595 | " 'Liam Carter', 'Olivia Bennett', 'Ethan Brooks', 'Liam Carter',\n", 596 | " 'Olivia Bennett', 'Ethan Brooks', 'Mason Reed', 'Ethan Brooks',\n", 597 | " 'Ethan Brooks', 'Ava Thompson', 'Mason Reed', 'Ethan Brooks',\n", 598 | " 'Mason Reed', 'Ava Thompson', 'Olivia Bennett', 'Olivia Bennett',\n", 599 | " 'Mason Reed', 'Liam Carter', 'Ava Thompson', 'Olivia Bennett',\n", 600 | " 'Ethan Brooks', 'Ava Thompson', 'Olivia Bennett', 'Mason Reed',\n", 601 | " 'Ava Thompson', 'Ava Thompson', 'Ethan Brooks', 'Liam Carter',\n", 602 | " 'Ava Thompson', 'Mason Reed', 'Olivia Bennett', 'Mason Reed',\n", 603 | " 'Ava Thompson', 'Ava Thompson', 'Liam Carter', 'Olivia Bennett',\n", 604 | " 'Ethan Brooks', 'Mason Reed', 'Liam Carter', 'Ethan Brooks',\n", 605 | " 'Mason Reed', 'Ethan Brooks', 'Ethan Brooks', 'Ethan Brooks',\n", 606 | " 'Liam Carter', 'Ethan Brooks', 'Ava Thompson', 'Liam Carter',\n", 607 | " 'Liam Carter', 'Olivia Bennett', 'Ethan Brooks', 'Mason Reed',\n", 608 | " 'Liam Carter', 'Ethan Brooks', 'Olivia Bennett', 'Ethan Brooks',\n", 609 | " 'Ethan Brooks', 'Olivia Bennett', 'Mason Reed', 'Mason Reed',\n", 610 | " 'Ava Thompson', 'Mason Reed', 'Liam Carter', 'Mason Reed',\n", 611 | " 'Liam Carter', 'Ava Thompson', 'Olivia Bennett', 'Olivia Bennett',\n", 612 | " 'Olivia Bennett', 'Liam Carter', 'Ethan Brooks', 'Liam Carter',\n", 613 | " 'Ethan Brooks', 'Ava Thompson', 'Liam Carter', 'Ava Thompson',\n", 614 | " 'Liam Carter', 'Olivia Bennett', 'Olivia Bennett',\n", 615 | " 'Olivia Bennett', 'Ethan Brooks', 'Olivia Bennett', 'Ethan Brooks',\n", 616 | " 'Ava Thompson', 'Liam Carter', 'Liam Carter', 'Mason Reed',\n", 617 | " 'Ethan Brooks', 'Olivia Bennett', 'Ava Thompson', 'Ava Thompson',\n", 618 | " 'Mason Reed', 'Ava Thompson', 'Mason Reed', 'Ava Thompson',\n", 619 | " 'Ethan Brooks', 'Olivia Bennett', 'Liam Carter', 'Mason Reed',\n", 620 | " 'Ethan Brooks', 'Liam Carter', 'Olivia Bennett', 'Liam Carter',\n", 621 | " 'Ethan Brooks', 'Liam Carter', 'Liam Carter', 'Ava Thompson',\n", 622 | " 'Mason Reed', 'Mason Reed', 'Ava Thompson', 'Olivia Bennett',\n", 623 | " 'Liam Carter', 'Ethan Brooks', 'Olivia Bennett', 'Liam Carter',\n", 624 | " 'Liam Carter', 'Ethan Brooks', 'Olivia Bennett', 'Liam Carter',\n", 625 | " 'Ethan Brooks', 'Ava Thompson', 'Mason Reed', 'Ethan Brooks',\n", 626 | " 'Olivia Bennett', 'Mason Reed', 'Liam Carter', 'Ethan Brooks',\n", 627 | " 'Mason Reed', 'Olivia Bennett', 'Olivia Bennett', 'Ethan Brooks',\n", 628 | " 'Ava Thompson', 'Olivia Bennett', 'Mason Reed', 'Ethan Brooks',\n", 629 | " 'Olivia Bennett', 'Olivia Bennett', 'Ava Thompson', 'Liam Carter',\n", 630 | " 'Ava Thompson', 'Olivia Bennett', 'Mason Reed', 'Mason Reed',\n", 631 | " 'Olivia Bennett', 'Liam Carter', 'Mason Reed', 'Ethan Brooks',\n", 632 | " 'Olivia Bennett', 'Liam Carter', 'Liam Carter', 'Liam Carter',\n", 633 | " 'Liam Carter', 'Ethan Brooks', 'Mason Reed', 'Ava Thompson',\n", 634 | " 'Mason Reed', 'Olivia Bennett', 'Ethan Brooks', 'Ethan Brooks',\n", 635 | " 'Mason Reed', 'Mason Reed', 'Olivia Bennett', 'Liam Carter',\n", 636 | " 'Ava Thompson', 'Liam Carter', 'Liam Carter', 'Ethan Brooks',\n", 637 | " 'Olivia Bennett', 'Mason Reed', 'Ethan Brooks', 'Mason Reed',\n", 638 | " 'Liam Carter', 'Mason Reed', 'Olivia Bennett', 'Ava Thompson',\n", 639 | " 'Mason Reed', 'Ava Thompson', 'Olivia Bennett', 'Ava Thompson',\n", 640 | " 'Ava Thompson', 'Olivia Bennett', 'Mason Reed', 'Ava Thompson',\n", 641 | " 'Ethan Brooks', 'Ethan Brooks', 'Ethan Brooks', 'Ethan Brooks',\n", 642 | " 'Ethan Brooks', 'Liam Carter', 'Liam Carter', 'Mason Reed',\n", 643 | " 'Liam Carter', 'Ethan Brooks', 'Liam Carter', 'Olivia Bennett',\n", 644 | " 'Mason Reed', 'Ava Thompson', 'Olivia Bennett', 'Mason Reed',\n", 645 | " 'Olivia Bennett', 'Ethan Brooks', 'Mason Reed', 'Olivia Bennett',\n", 646 | " 'Mason Reed', 'Liam Carter', 'Ava Thompson', 'Olivia Bennett',\n", 647 | " 'Ethan Brooks', 'Olivia Bennett', 'Ava Thompson', 'Olivia Bennett',\n", 648 | " 'Olivia Bennett', 'Mason Reed', 'Mason Reed', 'Ethan Brooks',\n", 649 | " 'Olivia Bennett', 'Mason Reed', 'Liam Carter', 'Ava Thompson',\n", 650 | " 'Ava Thompson', 'Ava Thompson', 'Mason Reed', 'Mason Reed',\n", 651 | " 'Mason Reed', 'Olivia Bennett', 'Liam Carter', 'Ethan Brooks',\n", 652 | " 'Ava Thompson', 'Ava Thompson', 'Liam Carter', 'Liam Carter',\n", 653 | " 'Ethan Brooks', 'Mason Reed', 'Ava Thompson', 'Olivia Bennett',\n", 654 | " 'Liam Carter', 'Olivia Bennett', 'Olivia Bennett', 'Ethan Brooks',\n", 655 | " 'Olivia Bennett', 'Mason Reed', 'Liam Carter', 'Liam Carter',\n", 656 | " 'Ava Thompson', 'Ava Thompson', 'Liam Carter', 'Ava Thompson',\n", 657 | " 'Olivia Bennett', 'Ava Thompson', 'Liam Carter', 'Liam Carter',\n", 658 | " 'Ethan Brooks', 'Ethan Brooks', 'Ava Thompson', 'Liam Carter',\n", 659 | " 'Ethan Brooks', 'Olivia Bennett', 'Ava Thompson', 'Olivia Bennett',\n", 660 | " 'Liam Carter', 'Mason Reed', 'Ethan Brooks', 'Mason Reed',\n", 661 | " 'Olivia Bennett', 'Ethan Brooks', 'Liam Carter', 'Liam Carter',\n", 662 | " 'Olivia Bennett', 'Mason Reed', 'Liam Carter', 'Mason Reed',\n", 663 | " 'Ava Thompson', 'Liam Carter', 'Mason Reed', 'Ethan Brooks',\n", 664 | " 'Olivia Bennett', 'Ava Thompson', 'Mason Reed', 'Olivia Bennett',\n", 665 | " 'Ethan Brooks', 'Liam Carter', 'Liam Carter', 'Ava Thompson',\n", 666 | " 'Mason Reed', 'Liam Carter', 'Mason Reed', 'Ava Thompson',\n", 667 | " 'Olivia Bennett', 'Ethan Brooks', 'Mason Reed', 'Olivia Bennett',\n", 668 | " 'Ethan Brooks', 'Mason Reed', 'Ava Thompson', 'Mason Reed',\n", 669 | " 'Ava Thompson', 'Olivia Bennett', 'Olivia Bennett', 'Mason Reed',\n", 670 | " 'Ava Thompson', 'Liam Carter', 'Mason Reed', 'Ava Thompson',\n", 671 | " 'Ethan Brooks', 'Olivia Bennett', 'Liam Carter', 'Liam Carter',\n", 672 | " 'Liam Carter', 'Ethan Brooks', 'Olivia Bennett', 'Mason Reed',\n", 673 | " 'Olivia Bennett', 'Ava Thompson', 'Olivia Bennett', 'Ava Thompson',\n", 674 | " 'Ethan Brooks', 'Olivia Bennett', 'Ava Thompson', 'Ava Thompson',\n", 675 | " 'Mason Reed', 'Liam Carter', 'Mason Reed', 'Mason Reed',\n", 676 | " 'Ethan Brooks', 'Ethan Brooks', 'Liam Carter', 'Liam Carter',\n", 677 | " 'Ethan Brooks', 'Mason Reed', 'Liam Carter', 'Olivia Bennett',\n", 678 | " 'Liam Carter', 'Ethan Brooks', 'Ethan Brooks', 'Olivia Bennett',\n", 679 | " 'Ava Thompson', 'Mason Reed', 'Ava Thompson', 'Ethan Brooks',\n", 680 | " 'Mason Reed', 'Olivia Bennett', 'Olivia Bennett', 'Liam Carter',\n", 681 | " 'Liam Carter', 'Liam Carter', 'Mason Reed', 'Mason Reed',\n", 682 | " 'Liam Carter', 'Ethan Brooks', 'Ava Thompson', 'Olivia Bennett',\n", 683 | " 'Ethan Brooks', 'Liam Carter', 'Olivia Bennett', 'Liam Carter',\n", 684 | " 'Ethan Brooks', 'Olivia Bennett', 'Mason Reed', 'Olivia Bennett',\n", 685 | " 'Olivia Bennett', 'Ethan Brooks', 'Ethan Brooks', 'Ethan Brooks',\n", 686 | " 'Mason Reed', 'Liam Carter', 'Liam Carter', 'Ethan Brooks',\n", 687 | " 'Olivia Bennett', 'Liam Carter', 'Mason Reed', 'Ava Thompson',\n", 688 | " 'Olivia Bennett', 'Olivia Bennett', 'Olivia Bennett', 'Mason Reed',\n", 689 | " 'Ava Thompson', 'Ethan Brooks', 'Ava Thompson', 'Liam Carter',\n", 690 | " 'Liam Carter', 'Olivia Bennett', 'Ava Thompson', 'Ava Thompson',\n", 691 | " 'Ava Thompson', 'Ethan Brooks', 'Liam Carter', 'Ava Thompson',\n", 692 | " 'Mason Reed', 'Olivia Bennett', 'Liam Carter', 'Mason Reed',\n", 693 | " 'Ava Thompson', 'Ethan Brooks', 'Ava Thompson', 'Liam Carter',\n", 694 | " 'Ava Thompson', 'Liam Carter', 'Ethan Brooks', 'Ava Thompson',\n", 695 | " 'Mason Reed', 'Mason Reed', 'Olivia Bennett', 'Olivia Bennett',\n", 696 | " 'Liam Carter', 'Ava Thompson', 'Liam Carter', 'Liam Carter',\n", 697 | " 'Ava Thompson', 'Olivia Bennett', 'Ethan Brooks', 'Liam Carter',\n", 698 | " 'Olivia Bennett', 'Liam Carter', 'Olivia Bennett', 'Ava Thompson',\n", 699 | " 'Ethan Brooks'], dtype=object)" 700 | ] 701 | }, 702 | "execution_count": 36, 703 | "metadata": {}, 704 | "output_type": "execute_result" 705 | } 706 | ], 707 | "source": [ 708 | "# predict test samples\n", 709 | "submission = best_model.predict(test)\n", 710 | "submission" 711 | ] 712 | }, 713 | { 714 | "cell_type": "markdown", 715 | "metadata": { 716 | "id": "wepRkQcrLbG1" 717 | }, 718 | "source": [ 719 | "

\n", 720 | "\n", 721 | "سلول جواب‌ساز\n", 722 | "\n", 723 | "

\n", 724 | "\n", 725 | "

\n", 726 | "\n", 727 | " برای ساخته‌شدن فایل result.zip سلول زیر را اجرا کنید. توجه داشته باشید که پیش از اجرای سلول زیر تغییرات اعمال شده در نت‌بوک را ذخیره کرده باشید (ctrl+s) در غیر این صورت، در پایان مسابقه نمره شما به صفر تغییر خواهد کرد.\n", 728 | "
\n", 729 | " همچنین اگر از کولب برای اجرای این فایل نوت‌بوک استفاده می‌کنید، قبل از ارسال فایل result.zip، آخرین نسخه‌ی نوت‌بوک خود را دانلود کرده و داخل فایل ارسالی قرار دهید.\n", 730 | "" 731 | ] 732 | }, 733 | { 734 | "cell_type": "code", 735 | "execution_count": null, 736 | "metadata": { 737 | "id": "fx5fwq5DLd2v" 738 | }, 739 | "outputs": [], 740 | "source": [ 741 | "import zipfile\n", 742 | "import joblib\n", 743 | "\n", 744 | "if not os.path.exists(os.path.join(os.getcwd(), 'Authors.ipynb')):\n", 745 | " %notebook -e Authors.ipynb\n", 746 | "\n", 747 | "def compress(file_names):\n", 748 | " print(\"File Paths:\")\n", 749 | " print(file_names)\n", 750 | " compression = zipfile.ZIP_DEFLATED\n", 751 | " with zipfile.ZipFile(\"result.zip\", mode=\"w\") as zf:\n", 752 | " for file_name in file_names:\n", 753 | " zf.write('./' + file_name, file_name, compress_type=compression)\n", 754 | "\n", 755 | "submission.to_csv('submission.csv', index=False)\n", 756 | "file_names = ['Authors.ipynb', 'submission.csv']\n", 757 | "compress(file_names)" 758 | ] 759 | } 760 | ], 761 | "metadata": { 762 | "colab": { 763 | "provenance": [] 764 | }, 765 | "kernelspec": { 766 | "display_name": "Python 3 (ipykernel)", 767 | "language": "python", 768 | "name": "python3" 769 | }, 770 | "language_info": { 771 | "codemirror_mode": { 772 | "name": "ipython", 773 | "version": 3 774 | }, 775 | "file_extension": ".py", 776 | "mimetype": "text/x-python", 777 | "name": "python", 778 | "nbconvert_exporter": "python", 779 | "pygments_lexer": "ipython3", 780 | "version": "3.11.4" 781 | } 782 | }, 783 | "nbformat": 4, 784 | "nbformat_minor": 1 785 | } 786 | -------------------------------------------------------------------------------- /challenge_4/ChandMidi.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "id": "d333515a", 6 | "metadata": { 7 | "id": "d333515a" 8 | }, 9 | "source": [ 10 | "

\n", 11 | "\n", 12 | "به فیلم چند میدی؟\n", 13 | "\n", 14 | "

" 15 | ] 16 | }, 17 | { 18 | "cell_type": "markdown", 19 | "id": "6ed20bff", 20 | "metadata": { 21 | "id": "6ed20bff" 22 | }, 23 | "source": [ 24 | "

\n", 25 | "\n", 26 | "مقدمه و صورت مسئله\n", 27 | "\n", 28 | "

\n", 29 | "\n", 30 | "

\n", 31 | "\n", 32 | "کاربران می‌توانند در وب‌سایت IMDb با توجه به تجربه‌شان از دیدن یک فیلم یا سریال، به آن هر امتیازی از ۱ تا ۱۰ بدهند و برای آن نقدی بنویسند. در این مسئله قصد داریم با استفاده از مجموعه‌ای از امتیازات و نقدهای کاربران برای بسیاری از فیلم‌ها و سریال‌ها، مدلی را آموزش دهیم که بتواند بر اساس نقد متنی نوشته شده، امتیاز داده شده را پیش‌بینی کند.\n", 33 | "\n", 34 | "

" 35 | ] 36 | }, 37 | { 38 | "cell_type": "markdown", 39 | "id": "c5af1784", 40 | "metadata": { 41 | "id": "c5af1784" 42 | }, 43 | "source": [ 44 | "

\n", 45 | "\n", 46 | "وارد کردن کتابخانه‌های مورد نیاز\n", 47 | "\n", 48 | "

\n", 49 | "\n", 50 | "

\n", 51 | "\n", 52 | " ابتدا کتابخانه‌های مورد نیازتان را وارد کنید.\n", 53 | "\n", 54 | "

" 55 | ] 56 | }, 57 | { 58 | "cell_type": "code", 59 | "execution_count": 1, 60 | "id": "99450f9e", 61 | "metadata": { 62 | "id": "99450f9e" 63 | }, 64 | "outputs": [], 65 | "source": [ 66 | "import pandas as pd\n", 67 | "import numpy as np" 68 | ] 69 | }, 70 | { 71 | "cell_type": "code", 72 | "source": [ 73 | "from google.colab import drive\n", 74 | "drive.mount('/content/drive')" 75 | ], 76 | "metadata": { 77 | "colab": { 78 | "base_uri": "https://localhost:8080/" 79 | }, 80 | "id": "0mVxKXAsHpTy", 81 | "outputId": "a908fd5a-2e97-4356-f609-1797df0c38d4" 82 | }, 83 | "id": "0mVxKXAsHpTy", 84 | "execution_count": 2, 85 | "outputs": [ 86 | { 87 | "output_type": "stream", 88 | "name": "stdout", 89 | "text": [ 90 | "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n" 91 | ] 92 | } 93 | ] 94 | }, 95 | { 96 | "cell_type": "markdown", 97 | "id": "72faaed8", 98 | "metadata": { 99 | "id": "72faaed8" 100 | }, 101 | "source": [ 102 | "

\n", 103 | "\n", 104 | "معرفی مجموعه داده\n", 105 | "\n", 106 | "

\n", 107 | "\n", 108 | "

\n", 109 | "\n", 110 | "مجموعه داده آموزشی شامل ۱۱۰۶۱ سطر است که در جدول زیر، توضیحات هر ستون آمده است.\n", 111 | "\n", 112 | "

\n", 113 | "\n", 114 | "
\n", 115 | "
\n", 116 | "\n", 117 | " \n", 118 | "|ستون|توضیحات|\n", 119 | "|:------:|:---:|\n", 120 | "|Movie_ID|شناسه‌ی یکتای هر فیلم در وبسایت IMDb|\n", 121 | "|Review|نقد نوشته شده برای این فیلم توسط کاربر|\n", 122 | "|Rating|امتیاز داده شده توسط کاربر|\n", 123 | "\n", 124 | " \n", 125 | "\n", 126 | "
\n", 127 | "
\n", 128 | "\n", 129 | "\n", 130 | "

\n", 131 | "\n", 132 | " مجموعه داده آزمایش نیز مانند مجموعه آموزش است با این تفاوت که ستون Rating که متغیر هدف مسئله است را در خود ندارد. مجموعه داده آزمایش ۲۷۶۵ سطر دارد.\n", 133 | "\n", 134 | "

\n" 135 | ] 136 | }, 137 | { 138 | "cell_type": "markdown", 139 | "id": "e99bbe9b", 140 | "metadata": { 141 | "id": "e99bbe9b" 142 | }, 143 | "source": [ 144 | "

\n", 145 | "\n", 146 | "خواندن مجموعه داده\n", 147 | "\n", 148 | "

\n", 149 | "\n", 150 | "

\n", 151 | "\n", 152 | " در ابتدا نیاز است فایل‌های مجموعه‌داده را بخوانید. نمونه‌های آموزشی در فایل train.csv و نمونه‌های آزمون که باید دسته‌ی آن‌ها را پیش‌بینی کنید در فایل test.csv ذخیره شده‌اند. اگر لازم دانستید می‌توانید به دلخواه خود بخشی از دادگان آموزشی را به عنوان دادگان اعتبارسنجی نیز جدا کنید.\n", 153 | "\n", 154 | "

" 155 | ] 156 | }, 157 | { 158 | "cell_type": "code", 159 | "execution_count": 41, 160 | "id": "7dcb9e87", 161 | "metadata": { 162 | "colab": { 163 | "base_uri": "https://localhost:8080/", 164 | "height": 178 165 | }, 166 | "id": "7dcb9e87", 167 | "outputId": "a59b1fb2-7366-4757-bea5-a46201f510c1" 168 | }, 169 | "outputs": [ 170 | { 171 | "output_type": "execute_result", 172 | "data": { 173 | "text/plain": [ 174 | "Movie_ID 0\n", 175 | "Review 0\n", 176 | "Rating 6\n", 177 | "dtype: int64" 178 | ], 179 | "text/html": [ 180 | "
\n", 181 | "\n", 194 | "\n", 195 | " \n", 196 | " \n", 197 | " \n", 198 | " \n", 199 | " \n", 200 | " \n", 201 | " \n", 202 | " \n", 203 | " \n", 204 | " \n", 205 | " \n", 206 | " \n", 207 | " \n", 208 | " \n", 209 | " \n", 210 | " \n", 211 | " \n", 212 | " \n", 213 | " \n", 214 | " \n", 215 | "
0
Movie_ID0
Review0
Rating6
\n", 216 | "

" 217 | ] 218 | }, 219 | "metadata": {}, 220 | "execution_count": 41 221 | } 222 | ], 223 | "source": [ 224 | "train_data = pd.read_csv('/content/drive/MyDrive/ChandMidi/ChandMidi/data/train.csv')\n", 225 | "train_data.isnull().sum()\n" 226 | ] 227 | }, 228 | { 229 | "cell_type": "code", 230 | "execution_count": 42, 231 | "id": "adb6ba5a", 232 | "metadata": { 233 | "colab": { 234 | "base_uri": "https://localhost:8080/", 235 | "height": 147 236 | }, 237 | "id": "adb6ba5a", 238 | "outputId": "1372b812-b2ce-45a5-beaf-2a4aa236d2ed" 239 | }, 240 | "outputs": [ 241 | { 242 | "output_type": "execute_result", 243 | "data": { 244 | "text/plain": [ 245 | "Movie_ID 0\n", 246 | "Review 0\n", 247 | "dtype: int64" 248 | ], 249 | "text/html": [ 250 | "
\n", 251 | "\n", 264 | "\n", 265 | " \n", 266 | " \n", 267 | " \n", 268 | " \n", 269 | " \n", 270 | " \n", 271 | " \n", 272 | " \n", 273 | " \n", 274 | " \n", 275 | " \n", 276 | " \n", 277 | " \n", 278 | " \n", 279 | " \n", 280 | " \n", 281 | "
0
Movie_ID0
Review0
\n", 282 | "

" 283 | ] 284 | }, 285 | "metadata": {}, 286 | "execution_count": 42 287 | } 288 | ], 289 | "source": [ 290 | "test_data = pd.read_csv('/content/drive/MyDrive/ChandMidi/ChandMidi/data/test.csv')\n", 291 | "test_data.isnull().sum()" 292 | ] 293 | }, 294 | { 295 | "cell_type": "markdown", 296 | "id": "23daec54", 297 | "metadata": { 298 | "id": "23daec54" 299 | }, 300 | "source": [ 301 | "

\n", 302 | "\n", 303 | "پیش‌پردازش و مهندسی ویژگی\n", 304 | "\n", 305 | "

\n", 306 | "\n", 307 | "

\n", 308 | "\n", 309 | " در این سوال شما می‌توانید از هر تکنیک پیش‌پردازش/مهندسی ویژگی که در گذشته آموختید، استفاده کنید.\n", 310 | "
\n", 311 | " تکنیک‌هایی که استفاده می‌کنید به شکل مستقیم مورد ارزیابی توسط سامانه داوری قرار نمی‌گیرند. بلکه همه آن‌ها در دقت مدل شما تاثیر خواهند گذاشت؛ بنابراین هر چه پیش‌پردازش/مهندسی ویژگی بهتری انجام دهید تا دقت مدل بهبود پیدا کند، امتیاز بیشتری از این سوال کسب خواهید کرد.\n", 312 | "\n", 313 | "

" 314 | ] 315 | }, 316 | { 317 | "cell_type": "code", 318 | "execution_count": 48, 319 | "id": "7e457c9f", 320 | "metadata": { 321 | "id": "7e457c9f" 322 | }, 323 | "outputs": [], 324 | "source": [ 325 | "from sklearn.model_selection import train_test_split\n", 326 | "from sklearn.preprocessing import LabelEncoder\n", 327 | "from tensorflow.keras.preprocessing.text import Tokenizer # Update import\n", 328 | "from tensorflow.keras.preprocessing.sequence import pad_sequences # Update import\n", 329 | "from tensorflow.keras.utils import to_categorical # Update\n", 330 | "\n", 331 | "\n", 332 | "\n", 333 | "\n", 334 | "\n", 335 | "\n", 336 | "train_data.dropna(inplace=True)\n", 337 | "\n", 338 | "X = train_data['Review']\n", 339 | "y = train_data['Rating']\n", 340 | "\n", 341 | "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)\n", 342 | "\n", 343 | "tokenizer = Tokenizer(num_words=10000) # Use the top 10,000 words\n", 344 | "tokenizer.fit_on_texts(X_train)\n", 345 | "\n", 346 | "X_train_seq = tokenizer.texts_to_sequences(X_train)\n", 347 | "X_val_seq = tokenizer.texts_to_sequences(X_val)\n", 348 | "\n", 349 | "max_length = 100 # Maximum length of sequences\n", 350 | "X_train_pad = pad_sequences(X_train_seq, maxlen=max_length)\n", 351 | "X_val_pad = pad_sequences(X_val_seq, maxlen=max_length)" 352 | ] 353 | }, 354 | { 355 | "cell_type": "markdown", 356 | "id": "f99f2c94", 357 | "metadata": { 358 | "id": "f99f2c94" 359 | }, 360 | "source": [ 361 | "

\n", 362 | "\n", 363 | "مدل‌سازی\n", 364 | "\n", 365 | "

\n", 366 | "\n", 367 | "

\n", 368 | "\n", 369 | " حال که داده را پاکسازی کرده‌اید، وقت آن است که مدلی آموزش دهید که بتواند متغیر هدف این مسئله را پیش‌بینی کند.\n", 370 | "
\n", 371 | " شما مجاز هستید از هر مدلی که آموخته‌اید استفاده کنید. به عبارت بهتر، هدف این سوال پیش‌بینی هرچه بهتر متغیر هدف مسئله است!\n", 372 | "\n", 373 | "

" 374 | ] 375 | }, 376 | { 377 | "cell_type": "code", 378 | "execution_count": 49, 379 | "id": "03d20a30", 380 | "metadata": { 381 | "colab": { 382 | "base_uri": "https://localhost:8080/", 383 | "height": 730 384 | }, 385 | "id": "03d20a30", 386 | "outputId": "be39634d-6616-4a4d-bf5e-f7841e060de1" 387 | }, 388 | "outputs": [ 389 | { 390 | "output_type": "stream", 391 | "name": "stderr", 392 | "text": [ 393 | "/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/embedding.py:90: UserWarning: Argument `input_length` is deprecated. Just remove it.\n", 394 | " warnings.warn(\n" 395 | ] 396 | }, 397 | { 398 | "output_type": "display_data", 399 | "data": { 400 | "text/plain": [ 401 | "\u001b[1mModel: \"sequential_1\"\u001b[0m\n" 402 | ], 403 | "text/html": [ 404 | "
Model: \"sequential_1\"\n",
405 |               "
\n" 406 | ] 407 | }, 408 | "metadata": {} 409 | }, 410 | { 411 | "output_type": "display_data", 412 | "data": { 413 | "text/plain": [ 414 | "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n", 415 | "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", 416 | "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n", 417 | "│ embedding_1 (\u001b[38;5;33mEmbedding\u001b[0m) │ ? │ \u001b[38;5;34m0\u001b[0m (unbuilt) │\n", 418 | "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n", 419 | "│ lstm_2 (\u001b[38;5;33mLSTM\u001b[0m) │ ? │ \u001b[38;5;34m0\u001b[0m (unbuilt) │\n", 420 | "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n", 421 | "│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m) │ ? │ \u001b[38;5;34m0\u001b[0m (unbuilt) │\n", 422 | "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n", 423 | "│ lstm_3 (\u001b[38;5;33mLSTM\u001b[0m) │ ? │ \u001b[38;5;34m0\u001b[0m (unbuilt) │\n", 424 | "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n", 425 | "│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m) │ ? │ \u001b[38;5;34m0\u001b[0m (unbuilt) │\n", 426 | "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n", 427 | "│ dense_1 (\u001b[38;5;33mDense\u001b[0m) │ ? │ \u001b[38;5;34m0\u001b[0m (unbuilt) │\n", 428 | "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n" 429 | ], 430 | "text/html": [ 431 | "
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432 |               "┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃\n",
433 |               "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
434 |               "│ embedding_1 (Embedding)              │ ?                           │     0 (unbuilt) │\n",
435 |               "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
436 |               "│ lstm_2 (LSTM)                        │ ?                           │     0 (unbuilt) │\n",
437 |               "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
438 |               "│ dropout_2 (Dropout)                  │ ?                           │     0 (unbuilt) │\n",
439 |               "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
440 |               "│ lstm_3 (LSTM)                        │ ?                           │     0 (unbuilt) │\n",
441 |               "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
442 |               "│ dropout_3 (Dropout)                  │ ?                           │     0 (unbuilt) │\n",
443 |               "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
444 |               "│ dense_1 (Dense)                      │ ?                           │     0 (unbuilt) │\n",
445 |               "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
446 |               "
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459 |               "
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472 |               "
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485 |               "
\n" 486 | ] 487 | }, 488 | "metadata": {} 489 | }, 490 | { 491 | "output_type": "stream", 492 | "name": "stdout", 493 | "text": [ 494 | "Epoch 1/10\n", 495 | "\u001b[1m277/277\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 128ms/step - loss: 11.8900 - mae: 2.8667 - val_loss: 8.2140 - val_mae: 2.4928\n", 496 | "Epoch 2/10\n", 497 | "\u001b[1m277/277\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m33s\u001b[0m 119ms/step - loss: 7.7119 - mae: 2.3066 - val_loss: 5.1092 - val_mae: 1.8277\n", 498 | "Epoch 3/10\n", 499 | "\u001b[1m277/277\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m33s\u001b[0m 119ms/step - loss: 4.4994 - mae: 1.6797 - val_loss: 3.9560 - val_mae: 1.5624\n", 500 | "Epoch 4/10\n", 501 | "\u001b[1m277/277\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m33s\u001b[0m 119ms/step - loss: 3.1753 - mae: 1.3934 - val_loss: 3.6208 - val_mae: 1.4491\n", 502 | "Epoch 5/10\n", 503 | "\u001b[1m277/277\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 120ms/step - loss: 2.4336 - mae: 1.1970 - val_loss: 3.4732 - val_mae: 1.3458\n", 504 | "Epoch 6/10\n", 505 | "\u001b[1m277/277\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 119ms/step - loss: 2.0727 - mae: 1.0929 - val_loss: 3.3033 - val_mae: 1.2898\n", 506 | "Epoch 7/10\n", 507 | "\u001b[1m277/277\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 120ms/step - loss: 1.7920 - mae: 1.0119 - val_loss: 3.2225 - val_mae: 1.2443\n", 508 | "Epoch 8/10\n", 509 | "\u001b[1m277/277\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m33s\u001b[0m 119ms/step - loss: 1.5495 - mae: 0.9435 - val_loss: 3.1968 - val_mae: 1.2253\n", 510 | "Epoch 9/10\n", 511 | "\u001b[1m277/277\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 119ms/step - loss: 1.4433 - mae: 0.8985 - val_loss: 3.1478 - val_mae: 1.2217\n", 512 | "Epoch 10/10\n", 513 | "\u001b[1m277/277\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 119ms/step - loss: 1.3444 - mae: 0.8613 - val_loss: 3.1164 - val_mae: 1.1758\n" 514 | ] 515 | } 516 | ], 517 | "source": [ 518 | "model = Sequential()\n", 519 | "model.add(Embedding(input_dim=10000, output_dim=128, input_length=max_length))\n", 520 | "model.add(LSTM(64, return_sequences=True))\n", 521 | "model.add(Dropout(0.5))\n", 522 | "model.add(LSTM(32))\n", 523 | "model.add(Dropout(0.5))\n", 524 | "model.add(Dense(1, activation='linear'))\n", 525 | "\n", 526 | "model.compile(loss='mean_squared_error', optimizer='adam', metrics=['mae'])\n", 527 | "\n", 528 | "model.summary()\n", 529 | "\n", 530 | "history = model.fit(X_train_pad, y_train,\n", 531 | " epochs=10,\n", 532 | " batch_size=32,\n", 533 | " validation_data=(X_val_pad, y_val))" 534 | ] 535 | }, 536 | { 537 | "cell_type": "markdown", 538 | "id": "33da5d2a", 539 | "metadata": { 540 | "id": "33da5d2a" 541 | }, 542 | "source": [ 543 | "

\n", 544 | "\n", 545 | "معیار ارزیابی\n", 546 | "\n", 547 | "

\n", 548 | "\n", 549 | "

\n", 550 | "\n", 551 | " معیاری که برای ارزیابی عملکرد مدل انتخاب کرده‌ایم، r2_score نام دارد.\n", 552 | "
\n", 553 | " این معیار، سنجه ارزیابی کیفیت مدل شماست. به عبارت بهتر در سامانه داوری هم از همین معیار برای نمره‌دهی استفاده شده است.\n", 554 | "
\n", 555 | " پیشنهاد می‌شود با توجه به این معیار، عملکرد مدل خود را بر روی مجموعه داده آموزش یا اعتبارسنجی ارزیابی کنید.\n", 556 | "\n", 557 | "

" 558 | ] 559 | }, 560 | { 561 | "cell_type": "code", 562 | "execution_count": 50, 563 | "id": "cee1b883", 564 | "metadata": { 565 | "colab": { 566 | "base_uri": "https://localhost:8080/" 567 | }, 568 | "id": "cee1b883", 569 | "outputId": "60b101c6-f988-4137-dd36-3a6b7976f3b7" 570 | }, 571 | "outputs": [ 572 | { 573 | "output_type": "stream", 574 | "name": "stdout", 575 | "text": [ 576 | "\u001b[1m70/70\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 33ms/step\n", 577 | "Validation R² Score: 0.6253987550735474\n" 578 | ] 579 | } 580 | ], 581 | "source": [ 582 | "# evaluate model\n", 583 | "from sklearn.metrics import r2_score\n", 584 | "y_val_pred = model.predict(X_val_pad)\n", 585 | "\n", 586 | "r2 = r2_score(y_val, y_val_pred)\n", 587 | "print(f'Validation R² Score: {r2}')\n" 588 | ] 589 | }, 590 | { 591 | "cell_type": "markdown", 592 | "id": "4e9984de", 593 | "metadata": { 594 | "id": "4e9984de" 595 | }, 596 | "source": [ 597 | "

\n", 598 | "\n", 599 | " پیش‌بینی برای داده تست و خروجی\n", 600 | "\n", 601 | "

\n", 602 | "\n", 603 | "

\n", 604 | "\n", 605 | " پس از مهندسی ویژگی و مدلسازی، الگوریتمی دارید که می‌تواند شما را از متغیرهای مستقل به متغیر هدف برساند.\n", 606 | "
\n", 607 | " از این مدل برای پیش‌بینی نمونه‌های موجود در داده تست استفاده کنید و نتایج را در قالب جدول (dataframe) زیر آماده کنید.\n", 608 | "\n", 609 | "

\n", 610 | "\n", 611 | "
\n", 612 | "\n", 613 | " \n", 614 | "|ستون|توضیحات|\n", 615 | "|------|---|\n", 616 | "|Rating|امتیاز داده شده توسط کاربر|\n", 617 | " \n", 618 | "\n", 619 | "
\n", 620 | "\n" 621 | ] 622 | }, 623 | { 624 | "cell_type": "markdown", 625 | "id": "c2f6d82d", 626 | "metadata": { 627 | "id": "c2f6d82d" 628 | }, 629 | "source": [ 630 | "

\n", 631 | "\n", 632 | " اسم دیتافریم باید submission باشد؛ در غیر این صورت، سامانه داوری نمی‌تواند تلاش‌ شما را ارزیابی کند.\n", 633 | "
\n", 634 | " این دیتافریم تنها شامل ۱ ستون با اسم Rating است و ۲۷۶۵ سطر دارد.\n", 635 | "
\n", 636 | " به ازای هر سطر موجود در دیتافریم test شما باید یک مقدار پیشبینی شده داشته باشید.\n", 637 | "
\n", 638 | " جدول زیر، ۵ سطر ابتدایی دیتافریم submission را نشان می‌دهد. البته در جواب شما، مقادیر ستون Rating ممکن است متفاوت باشد.\n", 639 | "\n", 640 | "

\n", 641 | "\n", 642 | "
\n", 643 | "\n", 644 | " \n", 645 | "|Rating|\n", 646 | "|-----|\n", 647 | "|1|\n", 648 | "|10|\n", 649 | "|3|\n", 650 | "|7|\n", 651 | "|2|\n", 652 | "\n", 653 | "\n", 654 | "
\n", 655 | "\n" 656 | ] 657 | }, 658 | { 659 | "cell_type": "code", 660 | "execution_count": 52, 661 | "id": "3d4a0844", 662 | "metadata": { 663 | "colab": { 664 | "base_uri": "https://localhost:8080/" 665 | }, 666 | "id": "3d4a0844", 667 | "outputId": "073f8f1b-d43b-4338-cb58-c7472704f364" 668 | }, 669 | "outputs": [ 670 | { 671 | "output_type": "stream", 672 | "name": "stdout", 673 | "text": [ 674 | "\u001b[1m87/87\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 26ms/step\n" 675 | ] 676 | } 677 | ], 678 | "source": [ 679 | "X_test_seq = tokenizer.texts_to_sequences(test_data['Review'])\n", 680 | "X_test_pad = pad_sequences(X_test_seq, maxlen=max_length)\n", 681 | "\n", 682 | "predictions = model.predict(X_test_pad)\n", 683 | "predicted_ratings = predictions.flatten()\n", 684 | "\n", 685 | "submission = pd.DataFrame({'Movie_ID': test_data['Movie_ID'], 'Rating': predicted_ratings})\n", 686 | "submission.to_csv('submission.csv', index=False)" 687 | ] 688 | }, 689 | { 690 | "cell_type": "markdown", 691 | "id": "0a77e7cd", 692 | "metadata": { 693 | "id": "0a77e7cd" 694 | }, 695 | "source": [ 696 | "

\n", 697 | "\n", 698 | "سلول جواب‌ساز\n", 699 | "\n", 700 | "

\n", 701 | "\n", 702 | "

\n", 703 | "\n", 704 | " برای ساخته‌شدن فایل result.zip سلول زیر را اجرا کنید. توجه داشته باشید که پیش از اجرای سلول زیر تغییرات اعمال شده در نت‌بوک را ذخیره کرده باشید (ctrl+s) تا در صورت نیاز به پشتیبانی امکان بررسی کد شما وجود داشته باشد.\n", 705 | "\n", 706 | "

" 707 | ] 708 | }, 709 | { 710 | "cell_type": "code", 711 | "execution_count": 53, 712 | "id": "2c3bcd76", 713 | "metadata": { 714 | "colab": { 715 | "base_uri": "https://localhost:8080/", 716 | "height": 356 717 | }, 718 | "id": "2c3bcd76", 719 | "outputId": "8b5a48ac-b33b-474c-b114-162291c26648" 720 | }, 721 | "outputs": [ 722 | { 723 | "output_type": "stream", 724 | "name": "stdout", 725 | "text": [ 726 | "File Paths:\n", 727 | "['ChandMidi.ipynb', 'submission.csv']\n" 728 | ] 729 | }, 730 | { 731 | "output_type": "error", 732 | "ename": "FileNotFoundError", 733 | "evalue": "[Errno 2] No such file or directory: './ChandMidi.ipynb'", 734 | "traceback": [ 735 | "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", 736 | "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", 737 | "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0msubmission\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'submission.csv'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0mfile_names\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'ChandMidi.ipynb'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'submission.csv'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mcompress\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile_names\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", 738 | "\u001b[0;32m\u001b[0m in \u001b[0;36mcompress\u001b[0;34m(file_names)\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mzipfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mZipFile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"result.zip\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"w\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mzf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfile_name\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfile_names\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mzf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'./'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mfile_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfile_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompress_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcompression\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0msubmission\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'submission.csv'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", 739 | "\u001b[0;32m/usr/lib/python3.10/zipfile.py\u001b[0m in \u001b[0;36mwrite\u001b[0;34m(self, filename, arcname, compress_type, compresslevel)\u001b[0m\n\u001b[1;32m 1752\u001b[0m )\n\u001b[1;32m 1753\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1754\u001b[0;31m zinfo = ZipInfo.from_file(filename, arcname,\n\u001b[0m\u001b[1;32m 1755\u001b[0m strict_timestamps=self._strict_timestamps)\n\u001b[1;32m 1756\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", 740 | "\u001b[0;32m/usr/lib/python3.10/zipfile.py\u001b[0m in \u001b[0;36mfrom_file\u001b[0;34m(cls, filename, arcname, strict_timestamps)\u001b[0m\n\u001b[1;32m 505\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPathLike\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 506\u001b[0m \u001b[0mfilename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfspath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 507\u001b[0;31m \u001b[0mst\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 508\u001b[0m \u001b[0misdir\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mS_ISDIR\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mst\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mst_mode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 509\u001b[0m \u001b[0mmtime\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlocaltime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mst\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mst_mtime\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", 741 | "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: './ChandMidi.ipynb'" 742 | ] 743 | } 744 | ], 745 | "source": [ 746 | "import zipfile\n", 747 | "import joblib\n", 748 | "\n", 749 | "def compress(file_names):\n", 750 | " print(\"File Paths:\")\n", 751 | " print(file_names)\n", 752 | " compression = zipfile.ZIP_DEFLATED\n", 753 | " with zipfile.ZipFile(\"result.zip\", mode=\"w\") as zf:\n", 754 | " for file_name in file_names:\n", 755 | " zf.write('./' + file_name, file_name, compress_type=compression)\n", 756 | "\n", 757 | "submission.to_csv('submission.csv', index=False)\n", 758 | "file_names = ['ChandMidi.ipynb', 'submission.csv']\n", 759 | "compress(file_names)" 760 | ] 761 | }, 762 | { 763 | "cell_type": "code", 764 | "source": [], 765 | "metadata": { 766 | "id": "ivD6nkbVYHfM" 767 | }, 768 | "id": "ivD6nkbVYHfM", 769 | "execution_count": null, 770 | "outputs": [] 771 | } 772 | ], 773 | "metadata": { 774 | "kernelspec": { 775 | "display_name": "Python 3 (ipykernel)", 776 | "language": "python", 777 | "name": "python3" 778 | }, 779 | "language_info": { 780 | "codemirror_mode": { 781 | "name": "ipython", 782 | "version": 3 783 | }, 784 | "file_extension": ".py", 785 | "mimetype": "text/x-python", 786 | "name": "python", 787 | "nbconvert_exporter": "python", 788 | "pygments_lexer": "ipython3", 789 | "version": "3.11.4" 790 | }, 791 | "colab": { 792 | "provenance": [] 793 | } 794 | }, 795 | "nbformat": 4, 796 | "nbformat_minor": 5 797 | } --------------------------------------------------------------------------------