done

2024-10-07 12:45:24 +04:00 · 2024-10-07 12:45:24 +04:00 · 851ccb3e02
commit 851ccb3e02
parent 7dde22d217
11 changed files with 21846 additions and 1464 deletions
--- a/data/boston_housing.csv
+++ b/data/boston_housing.csv
@ -0,0 +1,507 @@
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+3.56868,0,18.1,0,0.58,6.437,75,2.8965,24,666,20.2,393.37,14.36,23.2
+4.64689,0,18.1,0,0.614,6.98,67.6,2.5329,24,666,20.2,374.68,11.66,29.8
+8.05579,0,18.1,0,0.584,5.427,95.4,2.4298,24,666,20.2,352.58,18.14,13.8
+6.39312,0,18.1,0,0.584,6.162,97.4,2.206,24,666,20.2,302.76,24.1,13.3
+4.87141,0,18.1,0,0.614,6.484,93.6,2.3053,24,666,20.2,396.21,18.68,16.7
+15.0234,0,18.1,0,0.614,5.304,97.3,2.1007,24,666,20.2,349.48,24.91,12
+10.233,0,18.1,0,0.614,6.185,96.7,2.1705,24,666,20.2,379.7,18.03,14.6
+14.3337,0,18.1,0,0.614,6.229,88,1.9512,24,666,20.2,383.32,13.11,21.4
+5.82401,0,18.1,0,0.532,6.242,64.7,3.4242,24,666,20.2,396.9,10.74,23
+5.70818,0,18.1,0,0.532,6.75,74.9,3.3317,24,666,20.2,393.07,7.74,23.7
+5.73116,0,18.1,0,0.532,7.061,77,3.4106,24,666,20.2,395.28,7.01,25
+2.81838,0,18.1,0,0.532,5.762,40.3,4.0983,24,666,20.2,392.92,10.42,21.8
+2.37857,0,18.1,0,0.583,5.871,41.9,3.724,24,666,20.2,370.73,13.34,20.6
+3.67367,0,18.1,0,0.583,6.312,51.9,3.9917,24,666,20.2,388.62,10.58,21.2
+5.69175,0,18.1,0,0.583,6.114,79.8,3.5459,24,666,20.2,392.68,14.98,19.1
+4.83567,0,18.1,0,0.583,5.905,53.2,3.1523,24,666,20.2,388.22,11.45,20.6
+0.15086,0,27.74,0,0.609,5.454,92.7,1.8209,4,711,20.1,395.09,18.06,15.2
+0.18337,0,27.74,0,0.609,5.414,98.3,1.7554,4,711,20.1,344.05,23.97,7
+0.20746,0,27.74,0,0.609,5.093,98,1.8226,4,711,20.1,318.43,29.68,8.1
+0.10574,0,27.74,0,0.609,5.983,98.8,1.8681,4,711,20.1,390.11,18.07,13.6
+0.11132,0,27.74,0,0.609,5.983,83.5,2.1099,4,711,20.1,396.9,13.35,20.1
+0.17331,0,9.69,0,0.585,5.707,54,2.3817,6,391,19.2,396.9,12.01,21.8
+0.27957,0,9.69,0,0.585,5.926,42.6,2.3817,6,391,19.2,396.9,13.59,24.5
+0.17899,0,9.69,0,0.585,5.67,28.8,2.7986,6,391,19.2,393.29,17.6,23.1
+0.2896,0,9.69,0,0.585,5.39,72.9,2.7986,6,391,19.2,396.9,21.14,19.7
+0.26838,0,9.69,0,0.585,5.794,70.6,2.8927,6,391,19.2,396.9,14.1,18.3
+0.23912,0,9.69,0,0.585,6.019,65.3,2.4091,6,391,19.2,396.9,12.92,21.2
+0.17783,0,9.69,0,0.585,5.569,73.5,2.3999,6,391,19.2,395.77,15.1,17.5
+0.22438,0,9.69,0,0.585,6.027,79.7,2.4982,6,391,19.2,396.9,14.33,16.8
+0.06263,0,11.93,0,0.573,6.593,69.1,2.4786,1,273,21,391.99,9.67,22.4
+0.04527,0,11.93,0,0.573,6.12,76.7,2.2875,1,273,21,396.9,9.08,20.6
+0.06076,0,11.93,0,0.573,6.976,91,2.1675,1,273,21,396.9,5.64,23.9
+0.10959,0,11.93,0,0.573,6.794,89.3,2.3889,1,273,21,393.45,6.48,22
+0.04741,0,11.93,0,0.573,6.03,80.8,2.505,1,273,21,396.9,7.88,11.9
--- a/data/car_prices.csv
+++ b/data/car_prices.csv
--- a/data/dollar.csv
+++ b/data/dollar.csv
@ -1,244 +0,0 @@
-"my_date","my_value","bullet","bulletClass","label"
-"28.03.2023","76.5662","","",""
-"31.03.2023","77.0863","","",""
-"01.04.2023","77.3233","","",""
-"04.04.2023","77.9510","","",""
-"05.04.2023","79.3563","","",""
-"06.04.2023","79.4961","","",""
-"07.04.2023","80.6713","","",""
-"08.04.2023","82.3988","","",""
-"11.04.2023","81.7441","","",""
-"12.04.2023","82.1799","","",""
-"13.04.2023","82.0934","","",""
-"14.04.2023","81.6758","","",""
-"15.04.2023","81.5045","","",""
-"18.04.2023","81.6279","","",""
-"19.04.2023","81.6028","","",""
-"20.04.2023","81.6549","","",""
-"21.04.2023","81.6188","","",""
-"22.04.2023","81.4863","","",""
-"25.04.2023","81.2745","","",""
-"26.04.2023","81.5499","","",""
-"27.04.2023","81.6274","","",""
-"28.04.2023","81.5601","","",""
-"29.04.2023","80.5093","","",""
-"03.05.2023","79.9609","","",""
-"04.05.2023","79.3071","","",""
-"05.05.2023","78.6139","","",""
-"06.05.2023","76.8207","","",""
-"11.05.2023","76.6929","","",""
-"12.05.2023","75.8846","round","min-pulsating-bullet","мин"
-"13.05.2023","77.2041","","",""
-"16.05.2023","79.1004","","",""
-"17.05.2023","79.9798","","",""
-"18.05.2023","80.7642","","",""
-"19.05.2023","80.0366","","",""
-"20.05.2023","79.9093","","",""
-"23.05.2023","79.9379","","",""
-"24.05.2023","80.1665","","",""
-"25.05.2023","79.9669","","",""
-"26.05.2023","79.9841","","",""
-"27.05.2023","79.9667","","",""
-"30.05.2023","80.0555","","",""
-"31.05.2023","80.6872","","",""
-"01.06.2023","80.9942","","",""
-"02.06.2023","80.9657","","",""
-"03.06.2023","80.8756","","",""
-"06.06.2023","81.3294","","",""
-"07.06.2023","81.2502","","",""
-"08.06.2023","81.4581","","",""
-"09.06.2023","82.0930","","",""
-"10.06.2023","82.6417","","",""
-"14.06.2023","83.6405","","",""
-"15.06.2023","84.3249","","",""
-"16.06.2023","83.9611","","",""
-"17.06.2023","83.6498","","",""
-"20.06.2023","83.9866","","",""
-"21.06.2023","84.2336","","",""
-"22.06.2023","84.2467","","",""
-"23.06.2023","83.6077","","",""
-"24.06.2023","84.0793","","",""
-"27.06.2023","84.6642","","",""
-"28.06.2023","85.0504","","",""
-"29.06.2023","85.6192","","",""
-"30.06.2023","87.0341","","",""
-"01.07.2023","88.3844","","",""
-"04.07.2023","89.3255","","",""
-"05.07.2023","89.5450","","",""
-"06.07.2023","90.3380","","",""
-"07.07.2023","92.5695","","",""
-"08.07.2023","91.6879","","",""
-"11.07.2023","91.4931","","",""
-"12.07.2023","90.5045","","",""
-"13.07.2023","90.6253","","",""
-"14.07.2023","90.1757","","",""
-"15.07.2023","90.1190","","",""
-"18.07.2023","90.4217","","",""
-"19.07.2023","90.6906","","",""
-"20.07.2023","91.2046","","",""
-"21.07.2023","90.8545","","",""
-"22.07.2023","90.3846","","",""
-"25.07.2023","90.4890","","",""
-"26.07.2023","90.0945","","",""
-"27.07.2023","90.0468","","",""
-"28.07.2023","90.0225","","",""
-"29.07.2023","90.9783","","",""
-"01.08.2023","91.5923","","",""
-"02.08.2023","91.7755","","",""
-"03.08.2023","92.8410","","",""
-"04.08.2023","93.7792","","",""
-"05.08.2023","94.8076","","",""
-"08.08.2023","96.5668","","",""
-"09.08.2023","96.0755","","",""
-"10.08.2023","97.3999","","",""
-"11.08.2023","97.2794","","",""
-"12.08.2023","98.2066","","",""
-"15.08.2023","101.0399","","",""
-"16.08.2023","97.4217","","",""
-"17.08.2023","96.7045","","",""
-"18.08.2023","93.7460","","",""
-"19.08.2023","93.4047","","",""
-"22.08.2023","94.1424","","",""
-"23.08.2023","94.1185","","",""
-"24.08.2023","94.4421","","",""
-"25.08.2023","94.4007","","",""
-"26.08.2023","94.7117","","",""
-"29.08.2023","95.4717","","",""
-"30.08.2023","95.7070","","",""
-"31.08.2023","95.9283","","",""
-"01.09.2023","96.3344","","",""
-"02.09.2023","96.3411","","",""
-"05.09.2023","96.6199","","",""
-"06.09.2023","97.5383","","",""
-"07.09.2023","97.8439","","",""
-"08.09.2023","98.1961","","",""
-"09.09.2023","97.9241","","",""
-"12.09.2023","96.5083","","",""
-"13.09.2023","94.7035","","",""
-"14.09.2023","95.9794","","",""
-"15.09.2023","96.1609","","",""
-"16.09.2023","96.6338","","",""
-"19.09.2023","96.6472","","",""
-"20.09.2023","96.2236","","",""
-"21.09.2023","96.6172","","",""
-"22.09.2023","96.0762","","",""
-"23.09.2023","96.0419","","",""
-"26.09.2023","96.1456","","",""
-"27.09.2023","96.2378","","",""
-"28.09.2023","96.5000","","",""
-"29.09.2023","97.0018","","",""
-"30.09.2023","97.4147","","",""
-"03.10.2023","98.4785","","",""
-"04.10.2023","99.2677","","",""
-"05.10.2023","99.4555","","",""
-"06.10.2023","99.6762","","",""
-"07.10.2023","100.4911","","",""
-"10.10.2023","101.3598","round","max-pulsating-bullet","макс"
-"11.10.2023","99.9349","","",""
-"12.10.2023","99.9808","","",""
-"13.10.2023","96.9948","","",""
-"14.10.2023","97.3075","","",""
-"17.10.2023","97.2865","","",""
-"18.10.2023","97.3458","","",""
-"19.10.2023","97.3724","","",""
-"20.10.2023","97.3074","","",""
-"21.10.2023","95.9053","","",""
-"24.10.2023","94.7081","","",""
-"25.10.2023","93.5224","","",""
-"26.10.2023","93.1507","","",""
-"27.10.2023","93.5616","","",""
-"28.10.2023","93.2174","","",""
-"31.10.2023","93.2435","","",""
-"01.11.2023","92.0226","","",""
-"02.11.2023","93.2801","","",""
-"03.11.2023","93.1730","","",""
-"04.11.2023","93.0351","","",""
-"08.11.2023","92.4151","","",""
-"09.11.2023","92.1973","","",""
-"10.11.2023","91.9266","","",""
-"11.11.2023","92.0535","","",""
-"14.11.2023","92.1185","","",""
-"15.11.2023","91.2570","","",""
-"16.11.2023","89.4565","","",""
-"17.11.2023","88.9466","","",""
-"18.11.2023","89.1237","","",""
-"21.11.2023","88.4954","","",""
-"22.11.2023","87.8701","","",""
-"23.11.2023","88.1648","","",""
-"24.11.2023","88.1206","","",""
-"25.11.2023","88.8133","","",""
-"28.11.2023","88.7045","","",""
-"29.11.2023","88.6102","","",""
-"30.11.2023","88.8841","","",""
-"01.12.2023","88.5819","","",""
-"02.12.2023","89.7619","","",""
-"05.12.2023","90.6728","","",""
-"06.12.2023","91.5823","","",""
-"07.12.2023","92.7826","","",""
-"08.12.2023","92.5654","","",""
-"09.12.2023","91.6402","","",""
-"12.12.2023","90.9846","","",""
-"13.12.2023","90.2158","","",""
-"14.12.2023","89.8926","","",""
-"15.12.2023","89.6741","","",""
-"16.12.2023","89.6966","","",""
-"19.12.2023","90.4162","","",""
-"20.12.2023","90.0870","","",""
-"21.12.2023","90.4056","","",""
-"22.12.2023","91.7062","","",""
-"23.12.2023","91.9389","","",""
-"26.12.2023","91.9690","","",""
-"27.12.2023","91.7069","","",""
-"28.12.2023","91.7051","","",""
-"29.12.2023","90.3041","","",""
-"30.12.2023","89.6883","","",""
-"10.01.2024","90.4040","","",""
-"11.01.2024","89.3939","","",""
-"12.01.2024","88.7818","","",""
-"13.01.2024","88.1324","","",""
-"16.01.2024","87.6772","","",""
-"17.01.2024","87.6457","","",""
-"18.01.2024","88.3540","","",""
-"19.01.2024","88.6610","","",""
-"20.01.2024","88.5896","","",""
-"23.01.2024","87.9724","","",""
-"24.01.2024","87.9199","","",""
-"25.01.2024","88.2829","","",""
-"26.01.2024","88.6562","","",""
-"27.01.2024","89.5159","","",""
-"30.01.2024","89.6090","","",""
-"31.01.2024","89.2887","","",""
-"01.02.2024","89.6678","","",""
-"02.02.2024","90.2299","","",""
-"03.02.2024","90.6626","","",""
-"06.02.2024","91.2434","","",""
-"07.02.2024","90.6842","","",""
-"08.02.2024","91.1514","","",""
-"09.02.2024","91.2561","","",""
-"10.02.2024","90.8901","","",""
-"13.02.2024","91.0758","","",""
-"14.02.2024","91.2057","","",""
-"15.02.2024","91.4316","","",""
-"16.02.2024","91.8237","","",""
-"17.02.2024","92.5492","","",""
-"20.02.2024","92.4102","","",""
-"21.02.2024","92.3490","","",""
-"22.02.2024","92.4387","","",""
-"23.02.2024","92.7519","","",""
-"27.02.2024","92.6321","","",""
-"28.02.2024","92.0425","","",""
-"29.02.2024","91.8692","","",""
-"01.03.2024","90.8423","","",""
-"02.03.2024","91.3336","","",""
-"05.03.2024","91.3534","","",""
-"06.03.2024","91.1604","","",""
-"07.03.2024","90.3412","","",""
-"08.03.2024","90.7493","","",""
-"12.03.2024","90.6252","","",""
-"13.03.2024","90.8818","","",""
-"19.03.2024","91.9829","","",""
-"20.03.2024","92.2243","","",""
-"21.03.2024","92.6861","","",""
-"22.03.2024","91.9499","","",""
-"23.03.2024","92.6118","","",""
-"26.03.2024","92.7761","","",""
--- a/data/mobile_prices.csv
+++ b/data/mobile_prices.csv
--- a/data/titanic.csv
+++ b/data/titanic.csv
@ -1,892 +0,0 @@
-PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
-1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S
-2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C
-3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S
-4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S
-5,0,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S
-6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q
-7,0,1,"McCarthy, Mr. Timothy J",male,54,0,0,17463,51.8625,E46,S
-8,0,3,"Palsson, Master. Gosta Leonard",male,2,3,1,349909,21.075,,S
-9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,0,2,347742,11.1333,,S
-10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14,1,0,237736,30.0708,,C
-11,1,3,"Sandstrom, Miss. Marguerite Rut",female,4,1,1,PP 9549,16.7,G6,S
-12,1,1,"Bonnell, Miss. Elizabeth",female,58,0,0,113783,26.55,C103,S
-13,0,3,"Saundercock, Mr. William Henry",male,20,0,0,A/5. 2151,8.05,,S
-14,0,3,"Andersson, Mr. Anders Johan",male,39,1,5,347082,31.275,,S
-15,0,3,"Vestrom, Miss. Hulda Amanda Adolfina",female,14,0,0,350406,7.8542,,S
-16,1,2,"Hewlett, Mrs. (Mary D Kingcome) ",female,55,0,0,248706,16,,S
-17,0,3,"Rice, Master. Eugene",male,2,4,1,382652,29.125,,Q
-18,1,2,"Williams, Mr. Charles Eugene",male,,0,0,244373,13,,S
-19,0,3,"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)",female,31,1,0,345763,18,,S
-20,1,3,"Masselmani, Mrs. Fatima",female,,0,0,2649,7.225,,C
-21,0,2,"Fynney, Mr. Joseph J",male,35,0,0,239865,26,,S
-22,1,2,"Beesley, Mr. Lawrence",male,34,0,0,248698,13,D56,S
-23,1,3,"McGowan, Miss. Anna ""Annie""",female,15,0,0,330923,8.0292,,Q
-24,1,1,"Sloper, Mr. William Thompson",male,28,0,0,113788,35.5,A6,S
-25,0,3,"Palsson, Miss. Torborg Danira",female,8,3,1,349909,21.075,,S
-26,1,3,"Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)",female,38,1,5,347077,31.3875,,S
-27,0,3,"Emir, Mr. Farred Chehab",male,,0,0,2631,7.225,,C
-28,0,1,"Fortune, Mr. Charles Alexander",male,19,3,2,19950,263,C23 C25 C27,S
-29,1,3,"O'Dwyer, Miss. Ellen ""Nellie""",female,,0,0,330959,7.8792,,Q
-30,0,3,"Todoroff, Mr. Lalio",male,,0,0,349216,7.8958,,S
-31,0,1,"Uruchurtu, Don. Manuel E",male,40,0,0,PC 17601,27.7208,,C
-32,1,1,"Spencer, Mrs. William Augustus (Marie Eugenie)",female,,1,0,PC 17569,146.5208,B78,C
-33,1,3,"Glynn, Miss. Mary Agatha",female,,0,0,335677,7.75,,Q
-34,0,2,"Wheadon, Mr. Edward H",male,66,0,0,C.A. 24579,10.5,,S
-35,0,1,"Meyer, Mr. Edgar Joseph",male,28,1,0,PC 17604,82.1708,,C
-36,0,1,"Holverson, Mr. Alexander Oskar",male,42,1,0,113789,52,,S
-37,1,3,"Mamee, Mr. Hanna",male,,0,0,2677,7.2292,,C
-38,0,3,"Cann, Mr. Ernest Charles",male,21,0,0,A./5. 2152,8.05,,S
-39,0,3,"Vander Planke, Miss. Augusta Maria",female,18,2,0,345764,18,,S
-40,1,3,"Nicola-Yarred, Miss. Jamila",female,14,1,0,2651,11.2417,,C
-41,0,3,"Ahlin, Mrs. Johan (Johanna Persdotter Larsson)",female,40,1,0,7546,9.475,,S
-42,0,2,"Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott)",female,27,1,0,11668,21,,S
-43,0,3,"Kraeff, Mr. Theodor",male,,0,0,349253,7.8958,,C
-44,1,2,"Laroche, Miss. Simonne Marie Anne Andree",female,3,1,2,SC/Paris 2123,41.5792,,C
-45,1,3,"Devaney, Miss. Margaret Delia",female,19,0,0,330958,7.8792,,Q
-46,0,3,"Rogers, Mr. William John",male,,0,0,S.C./A.4. 23567,8.05,,S
-47,0,3,"Lennon, Mr. Denis",male,,1,0,370371,15.5,,Q
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-710,1,3,"Moubarek, Master. Halim Gonios (""William George"")",male,,1,1,2661,15.2458,,C
-711,1,1,"Mayne, Mlle. Berthe Antonine (""Mrs de Villiers"")",female,24,0,0,PC 17482,49.5042,C90,C
-712,0,1,"Klaber, Mr. Herman",male,,0,0,113028,26.55,C124,S
-713,1,1,"Taylor, Mr. Elmer Zebley",male,48,1,0,19996,52,C126,S
-714,0,3,"Larsson, Mr. August Viktor",male,29,0,0,7545,9.4833,,S
-715,0,2,"Greenberg, Mr. Samuel",male,52,0,0,250647,13,,S
-716,0,3,"Soholt, Mr. Peter Andreas Lauritz Andersen",male,19,0,0,348124,7.65,F G73,S
-717,1,1,"Endres, Miss. Caroline Louise",female,38,0,0,PC 17757,227.525,C45,C
-718,1,2,"Troutt, Miss. Edwina Celia ""Winnie""",female,27,0,0,34218,10.5,E101,S
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-720,0,3,"Johnson, Mr. Malkolm Joackim",male,33,0,0,347062,7.775,,S
-721,1,2,"Harper, Miss. Annie Jessie ""Nina""",female,6,0,1,248727,33,,S
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-723,0,2,"Gillespie, Mr. William Henry",male,34,0,0,12233,13,,S
-724,0,2,"Hodges, Mr. Henry Price",male,50,0,0,250643,13,,S
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-726,0,3,"Oreskovic, Mr. Luka",male,20,0,0,315094,8.6625,,S
-727,1,2,"Renouf, Mrs. Peter Henry (Lillian Jefferys)",female,30,3,0,31027,21,,S
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-729,0,2,"Bryhl, Mr. Kurt Arnold Gottfrid",male,25,1,0,236853,26,,S
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-744,0,3,"McNamee, Mr. Neal",male,24,1,0,376566,16.1,,S
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-747,0,3,"Abbott, Mr. Rossmore Edward",male,16,1,1,C.A. 2673,20.25,,S
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-751,1,2,"Wells, Miss. Joan",female,4,1,1,29103,23,,S
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-753,0,3,"Vande Velde, Mr. Johannes Joseph",male,33,0,0,345780,9.5,,S
-754,0,3,"Jonkoff, Mr. Lalio",male,23,0,0,349204,7.8958,,S
-755,1,2,"Herman, Mrs. Samuel (Jane Laver)",female,48,1,2,220845,65,,S
-756,1,2,"Hamalainen, Master. Viljo",male,0.67,1,1,250649,14.5,,S
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-766,1,1,"Hogeboom, Mrs. John C (Anna Andrews)",female,51,1,0,13502,77.9583,D11,S
-767,0,1,"Brewe, Dr. Arthur Jackson",male,,0,0,112379,39.6,,C
-768,0,3,"Mangan, Miss. Mary",female,30.5,0,0,364850,7.75,,Q
-769,0,3,"Moran, Mr. Daniel J",male,,1,0,371110,24.15,,Q
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-771,0,3,"Lievens, Mr. Rene Aime",male,24,0,0,345781,9.5,,S
-772,0,3,"Jensen, Mr. Niels Peder",male,48,0,0,350047,7.8542,,S
-773,0,2,"Mack, Mrs. (Mary)",female,57,0,0,S.O./P.P. 3,10.5,E77,S
-774,0,3,"Elias, Mr. Dibo",male,,0,0,2674,7.225,,C
-775,1,2,"Hocking, Mrs. Elizabeth (Eliza Needs)",female,54,1,3,29105,23,,S
-776,0,3,"Myhrman, Mr. Pehr Fabian Oliver Malkolm",male,18,0,0,347078,7.75,,S
-777,0,3,"Tobin, Mr. Roger",male,,0,0,383121,7.75,F38,Q
-778,1,3,"Emanuel, Miss. Virginia Ethel",female,5,0,0,364516,12.475,,S
-779,0,3,"Kilgannon, Mr. Thomas J",male,,0,0,36865,7.7375,,Q
-780,1,1,"Robert, Mrs. Edward Scott (Elisabeth Walton McMillan)",female,43,0,1,24160,211.3375,B3,S
-781,1,3,"Ayoub, Miss. Banoura",female,13,0,0,2687,7.2292,,C
-782,1,1,"Dick, Mrs. Albert Adrian (Vera Gillespie)",female,17,1,0,17474,57,B20,S
-783,0,1,"Long, Mr. Milton Clyde",male,29,0,0,113501,30,D6,S
-784,0,3,"Johnston, Mr. Andrew G",male,,1,2,W./C. 6607,23.45,,S
-785,0,3,"Ali, Mr. William",male,25,0,0,SOTON/O.Q. 3101312,7.05,,S
-786,0,3,"Harmer, Mr. Abraham (David Lishin)",male,25,0,0,374887,7.25,,S
-787,1,3,"Sjoblom, Miss. Anna Sofia",female,18,0,0,3101265,7.4958,,S
-788,0,3,"Rice, Master. George Hugh",male,8,4,1,382652,29.125,,Q
-789,1,3,"Dean, Master. Bertram Vere",male,1,1,2,C.A. 2315,20.575,,S
-790,0,1,"Guggenheim, Mr. Benjamin",male,46,0,0,PC 17593,79.2,B82 B84,C
-791,0,3,"Keane, Mr. Andrew ""Andy""",male,,0,0,12460,7.75,,Q
-792,0,2,"Gaskell, Mr. Alfred",male,16,0,0,239865,26,,S
-793,0,3,"Sage, Miss. Stella Anna",female,,8,2,CA. 2343,69.55,,S
-794,0,1,"Hoyt, Mr. William Fisher",male,,0,0,PC 17600,30.6958,,C
-795,0,3,"Dantcheff, Mr. Ristiu",male,25,0,0,349203,7.8958,,S
-796,0,2,"Otter, Mr. Richard",male,39,0,0,28213,13,,S
-797,1,1,"Leader, Dr. Alice (Farnham)",female,49,0,0,17465,25.9292,D17,S
-798,1,3,"Osman, Mrs. Mara",female,31,0,0,349244,8.6833,,S
-799,0,3,"Ibrahim Shawah, Mr. Yousseff",male,30,0,0,2685,7.2292,,C
-800,0,3,"Van Impe, Mrs. Jean Baptiste (Rosalie Paula Govaert)",female,30,1,1,345773,24.15,,S
-801,0,2,"Ponesell, Mr. Martin",male,34,0,0,250647,13,,S
-802,1,2,"Collyer, Mrs. Harvey (Charlotte Annie Tate)",female,31,1,1,C.A. 31921,26.25,,S
-803,1,1,"Carter, Master. William Thornton II",male,11,1,2,113760,120,B96 B98,S
-804,1,3,"Thomas, Master. Assad Alexander",male,0.42,0,1,2625,8.5167,,C
-805,1,3,"Hedman, Mr. Oskar Arvid",male,27,0,0,347089,6.975,,S
-806,0,3,"Johansson, Mr. Karl Johan",male,31,0,0,347063,7.775,,S
-807,0,1,"Andrews, Mr. Thomas Jr",male,39,0,0,112050,0,A36,S
-808,0,3,"Pettersson, Miss. Ellen Natalia",female,18,0,0,347087,7.775,,S
-809,0,2,"Meyer, Mr. August",male,39,0,0,248723,13,,S
-810,1,1,"Chambers, Mrs. Norman Campbell (Bertha Griggs)",female,33,1,0,113806,53.1,E8,S
-811,0,3,"Alexander, Mr. William",male,26,0,0,3474,7.8875,,S
-812,0,3,"Lester, Mr. James",male,39,0,0,A/4 48871,24.15,,S
-813,0,2,"Slemen, Mr. Richard James",male,35,0,0,28206,10.5,,S
-814,0,3,"Andersson, Miss. Ebba Iris Alfrida",female,6,4,2,347082,31.275,,S
-815,0,3,"Tomlin, Mr. Ernest Portage",male,30.5,0,0,364499,8.05,,S
-816,0,1,"Fry, Mr. Richard",male,,0,0,112058,0,B102,S
-817,0,3,"Heininen, Miss. Wendla Maria",female,23,0,0,STON/O2. 3101290,7.925,,S
-818,0,2,"Mallet, Mr. Albert",male,31,1,1,S.C./PARIS 2079,37.0042,,C
-819,0,3,"Holm, Mr. John Fredrik Alexander",male,43,0,0,C 7075,6.45,,S
-820,0,3,"Skoog, Master. Karl Thorsten",male,10,3,2,347088,27.9,,S
-821,1,1,"Hays, Mrs. Charles Melville (Clara Jennings Gregg)",female,52,1,1,12749,93.5,B69,S
-822,1,3,"Lulic, Mr. Nikola",male,27,0,0,315098,8.6625,,S
-823,0,1,"Reuchlin, Jonkheer. John George",male,38,0,0,19972,0,,S
-824,1,3,"Moor, Mrs. (Beila)",female,27,0,1,392096,12.475,E121,S
-825,0,3,"Panula, Master. Urho Abraham",male,2,4,1,3101295,39.6875,,S
-826,0,3,"Flynn, Mr. John",male,,0,0,368323,6.95,,Q
-827,0,3,"Lam, Mr. Len",male,,0,0,1601,56.4958,,S
-828,1,2,"Mallet, Master. Andre",male,1,0,2,S.C./PARIS 2079,37.0042,,C
-829,1,3,"McCormack, Mr. Thomas Joseph",male,,0,0,367228,7.75,,Q
-830,1,1,"Stone, Mrs. George Nelson (Martha Evelyn)",female,62,0,0,113572,80,B28,
-831,1,3,"Yasbeck, Mrs. Antoni (Selini Alexander)",female,15,1,0,2659,14.4542,,C
-832,1,2,"Richards, Master. George Sibley",male,0.83,1,1,29106,18.75,,S
-833,0,3,"Saad, Mr. Amin",male,,0,0,2671,7.2292,,C
-834,0,3,"Augustsson, Mr. Albert",male,23,0,0,347468,7.8542,,S
-835,0,3,"Allum, Mr. Owen George",male,18,0,0,2223,8.3,,S
-836,1,1,"Compton, Miss. Sara Rebecca",female,39,1,1,PC 17756,83.1583,E49,C
-837,0,3,"Pasic, Mr. Jakob",male,21,0,0,315097,8.6625,,S
-838,0,3,"Sirota, Mr. Maurice",male,,0,0,392092,8.05,,S
-839,1,3,"Chip, Mr. Chang",male,32,0,0,1601,56.4958,,S
-840,1,1,"Marechal, Mr. Pierre",male,,0,0,11774,29.7,C47,C
-841,0,3,"Alhomaki, Mr. Ilmari Rudolf",male,20,0,0,SOTON/O2 3101287,7.925,,S
-842,0,2,"Mudd, Mr. Thomas Charles",male,16,0,0,S.O./P.P. 3,10.5,,S
-843,1,1,"Serepeca, Miss. Augusta",female,30,0,0,113798,31,,C
-844,0,3,"Lemberopolous, Mr. Peter L",male,34.5,0,0,2683,6.4375,,C
-845,0,3,"Culumovic, Mr. Jeso",male,17,0,0,315090,8.6625,,S
-846,0,3,"Abbing, Mr. Anthony",male,42,0,0,C.A. 5547,7.55,,S
-847,0,3,"Sage, Mr. Douglas Bullen",male,,8,2,CA. 2343,69.55,,S
-848,0,3,"Markoff, Mr. Marin",male,35,0,0,349213,7.8958,,C
-849,0,2,"Harper, Rev. John",male,28,0,1,248727,33,,S
-850,1,1,"Goldenberg, Mrs. Samuel L (Edwiga Grabowska)",female,,1,0,17453,89.1042,C92,C
-851,0,3,"Andersson, Master. Sigvard Harald Elias",male,4,4,2,347082,31.275,,S
-852,0,3,"Svensson, Mr. Johan",male,74,0,0,347060,7.775,,S
-853,0,3,"Boulos, Miss. Nourelain",female,9,1,1,2678,15.2458,,C
-854,1,1,"Lines, Miss. Mary Conover",female,16,0,1,PC 17592,39.4,D28,S
-855,0,2,"Carter, Mrs. Ernest Courtenay (Lilian Hughes)",female,44,1,0,244252,26,,S
-856,1,3,"Aks, Mrs. Sam (Leah Rosen)",female,18,0,1,392091,9.35,,S
-857,1,1,"Wick, Mrs. George Dennick (Mary Hitchcock)",female,45,1,1,36928,164.8667,,S
-858,1,1,"Daly, Mr. Peter Denis ",male,51,0,0,113055,26.55,E17,S
-859,1,3,"Baclini, Mrs. Solomon (Latifa Qurban)",female,24,0,3,2666,19.2583,,C
-860,0,3,"Razi, Mr. Raihed",male,,0,0,2629,7.2292,,C
-861,0,3,"Hansen, Mr. Claus Peter",male,41,2,0,350026,14.1083,,S
-862,0,2,"Giles, Mr. Frederick Edward",male,21,1,0,28134,11.5,,S
-863,1,1,"Swift, Mrs. Frederick Joel (Margaret Welles Barron)",female,48,0,0,17466,25.9292,D17,S
-864,0,3,"Sage, Miss. Dorothy Edith ""Dolly""",female,,8,2,CA. 2343,69.55,,S
-865,0,2,"Gill, Mr. John William",male,24,0,0,233866,13,,S
-866,1,2,"Bystrom, Mrs. (Karolina)",female,42,0,0,236852,13,,S
-867,1,2,"Duran y More, Miss. Asuncion",female,27,1,0,SC/PARIS 2149,13.8583,,C
-868,0,1,"Roebling, Mr. Washington Augustus II",male,31,0,0,PC 17590,50.4958,A24,S
-869,0,3,"van Melkebeke, Mr. Philemon",male,,0,0,345777,9.5,,S
-870,1,3,"Johnson, Master. Harold Theodor",male,4,1,1,347742,11.1333,,S
-871,0,3,"Balkic, Mr. Cerin",male,26,0,0,349248,7.8958,,S
-872,1,1,"Beckwith, Mrs. Richard Leonard (Sallie Monypeny)",female,47,1,1,11751,52.5542,D35,S
-873,0,1,"Carlsson, Mr. Frans Olof",male,33,0,0,695,5,B51 B53 B55,S
-874,0,3,"Vander Cruyssen, Mr. Victor",male,47,0,0,345765,9,,S
-875,1,2,"Abelson, Mrs. Samuel (Hannah Wizosky)",female,28,1,0,P/PP 3381,24,,C
-876,1,3,"Najib, Miss. Adele Kiamie ""Jane""",female,15,0,0,2667,7.225,,C
-877,0,3,"Gustafsson, Mr. Alfred Ossian",male,20,0,0,7534,9.8458,,S
-878,0,3,"Petroff, Mr. Nedelio",male,19,0,0,349212,7.8958,,S
-879,0,3,"Laleff, Mr. Kristo",male,,0,0,349217,7.8958,,S
-880,1,1,"Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)",female,56,0,1,11767,83.1583,C50,C
-881,1,2,"Shelley, Mrs. William (Imanita Parrish Hall)",female,25,0,1,230433,26,,S
-882,0,3,"Markun, Mr. Johann",male,33,0,0,349257,7.8958,,S
-883,0,3,"Dahlberg, Miss. Gerda Ulrika",female,22,0,0,7552,10.5167,,S
-884,0,2,"Banfield, Mr. Frederick James",male,28,0,0,C.A./SOTON 34068,10.5,,S
-885,0,3,"Sutehall, Mr. Henry Jr",male,25,0,0,SOTON/OQ 392076,7.05,,S
-886,0,3,"Rice, Mrs. William (Margaret Norton)",female,39,0,5,382652,29.125,,Q
-887,0,2,"Montvila, Rev. Juozas",male,27,0,0,211536,13,,S
-888,1,1,"Graham, Miss. Margaret Edith",female,19,0,0,112053,30,B42,S
-889,0,3,"Johnston, Miss. Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,,S
-890,1,1,"Behr, Mr. Karl Howell",male,26,0,0,111369,30,C148,C
-891,0,3,"Dooley, Mr. Patrick",male,32,0,0,370376,7.75,,Q
--- a/lab1.ipynb
+++ b/lab1.ipynb
--- a/lab2.1.ipynb
+++ b/lab2.1.ipynb
@ -0,0 +1,162 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn.feature_selection import mutual_info_regression\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from imblearn.over_sampling import ADASYN\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "# Загрузка датасета с мобильными телефонами\n",
+    "df = pd.read_csv(\"data/mobile_prices.csv\")\n",
+    "print(df.columns)\n",
+    "\n",
+    "# Кодируем все строковые столбцы в числовые\n",
+    "label_encoders = {}\n",
+    "for col in df.select_dtypes(include=[\"object\"]).columns:\n",
+    "    le = LabelEncoder()\n",
+    "    df[col] = le.fit_transform(df[col])\n",
+    "    label_encoders[col] = le\n",
+    "\n",
+    "# Проверка на пропуски и \"зашумленные\" столбцы\n",
+    "noisy_features = []\n",
+    "for col in df.columns:\n",
+    "    if df[col].isnull().sum() / len(df) > 0.1:  # Если более 10% пропусков\n",
+    "        noisy_features.append(col)\n",
+    "print(f\"Зашумленные столбцы: {noisy_features}\")\n",
+    "\n",
+    "# Проверка на смещение\n",
+    "skewness = df.skew()\n",
+    "print(f\"Смещение: {skewness}\")\n",
+    "\n",
+    "skewed_features = skewness[abs(skewness) > 1].index.tolist()\n",
+    "print(f\"Сильно смещенные столбцы: {skewed_features}\")\n",
+    "\n",
+    "# Поиск выбросов\n",
+    "for col in df.select_dtypes(include=[\"number\"]).columns:\n",
+    "    Q1 = df[col].quantile(0.25)\n",
+    "    Q3 = df[col].quantile(0.75)\n",
+    "    IQR = Q3 - Q1\n",
+    "    lower_bound = Q1 - 1.5 * IQR\n",
+    "    upper_bound = Q3 + 1.5 * IQR\n",
+    "    outliers = df[col][(df[col] < lower_bound) | (df[col] > upper_bound)]\n",
+    "    print(f\"Выбросы в столбце '{col}':\\n{outliers}\\n\")\n",
+    "\n",
+    "# Визуализация выбросов\n",
+    "numeric_cols = df.select_dtypes(include=[\"number\"]).columns\n",
+    "\n",
+    "plt.figure(figsize=(12, 8))\n",
+    "for i, col in enumerate(numeric_cols, 1):\n",
+    "    plt.subplot(len(numeric_cols) // 3 + 1, 3, i)\n",
+    "    sns.boxplot(data=df, x=col)\n",
+    "    plt.title(f\"Boxplot for {col}\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# Логарифмирование признака 'Battery'\n",
+    "df[\"log_Battery\"] = np.log(df[\"Battery\"] + 1)\n",
+    "\n",
+    "# Заполнение пропусков\n",
+    "df[\"Battery\"] = df[\"Battery\"].fillna(df[\"Battery\"].mean())\n",
+    "\n",
+    "\n",
+    "# Функция для разбиения на train/val/test\n",
+    "def split_stratified_into_train_val_test(\n",
+    "    df_input,\n",
+    "    stratify_colname=\"y\",\n",
+    "    frac_train=0.6,\n",
+    "    frac_val=0.15,\n",
+    "    frac_test=0.25,\n",
+    "    random_state=None,\n",
+    "):\n",
+    "    if frac_train + frac_val + frac_test != 1.0:\n",
+    "        raise ValueError(\n",
+    "            \"fractions %f, %f, %f do not add up to 1.0\"\n",
+    "            % (frac_train, frac_val, frac_test)\n",
+    "        )\n",
+    "\n",
+    "    if stratify_colname not in df_input.columns:\n",
+    "        raise ValueError(\"%s is not a column in the dataframe\" % (stratify_colname))\n",
+    "\n",
+    "    X = df_input\n",
+    "    y = df_input[[stratify_colname]]\n",
+    "\n",
+    "    df_train, df_temp, y_train, y_temp = train_test_split(\n",
+    "        X, y, stratify=y, test_size=(1.0 - frac_train), random_state=random_state\n",
+    "    )\n",
+    "    relative_frac_test = frac_test / (frac_val + frac_test)\n",
+    "    df_val, df_test, y_val, y_test = train_test_split(\n",
+    "        df_temp,\n",
+    "        y_temp,\n",
+    "        stratify=y_temp,\n",
+    "        test_size=relative_frac_test,\n",
+    "        random_state=random_state,\n",
+    "    )\n",
+    "\n",
+    "    assert len(df_input) == len(df_train) + len(df_val) + len(df_test)\n",
+    "\n",
+    "    return df_train, df_val, df_test\n",
+    "\n",
+    "\n",
+    "# Разбиение на train/val/test\n",
+    "data = df[[\"Ram\", \"Price\", \"company\"]].copy()\n",
+    "\n",
+    "print(\"@data\", data)\n",
+    "\n",
+    "data = data.groupby(\"company\").filter(\n",
+    "    lambda x: len(x) > 4\n",
+    ")  # убираем классы с одним элементом\n",
+    "\n",
+    "df_train, df_val, df_test = split_stratified_into_train_val_test(\n",
+    "    data, stratify_colname=\"company\", frac_train=0.60, frac_val=0.20, frac_test=0.20\n",
+    ")\n",
+    "\n",
+    "print(\"Обучающая выборка: \", df_train.shape)\n",
+    "print(df_train[\"Ram\"].value_counts())\n",
+    "\n",
+    "print(\"Контрольная выборка: \", df_val.shape)\n",
+    "print(df_val[\"Ram\"].value_counts())\n",
+    "\n",
+    "print(\"Тестовая выборка: \", df_test.shape)\n",
+    "print(df_test[\"Ram\"].value_counts())\n",
+    "\n",
+    "# # Применение ADASYN для oversampling\n",
+    "# ada = ADASYN(n_neighbors=2)\n",
+    "# X_resampled, y_resampled = ada.fit_resample(df_train, df_train[\"company\"])\n",
+    "# df_train_adasyn = pd.DataFrame(X_resampled)\n",
+    "\n",
+    "# print(\"Обучающая выборка после oversampling: \", df_train_adasyn.shape)\n",
+    "# print(df_train_adasyn[\"Ram\"].value_counts())"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
--- a/lab2.2.ipynb
+++ b/lab2.2.ipynb
--- a/lec1.ipynb
+++ b/lec1.ipynb
@ -1,327 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Работа с NumPy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "matrix = np.array([[4, 5, 0], [10, 9, 9]])\n",
-    "print(\"matrix = \\n\", matrix, \"\\n\")\n",
-    "\n",
-    "tmatrix = matrix.T\n",
-    "print(\"tmatrix = \\n\", tmatrix, \"\\n\")\n",
-    "\n",
-    "vector = np.ravel(matrix)\n",
-    "print(\"vector = \\n\", vector, \"\\n\")\n",
-    "\n",
-    "tvector = np.reshape(vector, (6, 1))\n",
-    "print(\"tvector = \\n\", tvector, \"\\n\")\n",
-    "\n",
-    "list_matrix = list(matrix)\n",
-    "print(\"list_matrix = \\n\", list_matrix, \"\\n\")\n",
-    "\n",
-    "str_matrix = str(matrix)\n",
-    "print(\"matrix as str = \\n\", str_matrix, \"\\n\")\n",
-    "\n",
-    "print(\"matrix type is\", type(matrix), \"\\n\")\n",
-    "\n",
-    "print(\"vector type is\", type(vector), \"\\n\")\n",
-    "\n",
-    "print(\"list_matrix type is\", type(list_matrix), \"\\n\")\n",
-    "\n",
-    "print(\"str_matrix type is\", type(str_matrix), \"\\n\")\n",
-    "\n",
-    "formatted_vector = \"; \".join(map(str, vector))\n",
-    "print(\"formatted_vector = \\n\", formatted_vector, \"\\n\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Работа с Pandas DataFrame\n",
-    "\n",
-    "https://pandas.pydata.org/docs/user_guide/10min.html"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Работа с данными - чтение и запись CSV"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "df = pd.read_csv(\"data/titanic.csv\", index_col=\"PassengerId\")\n",
-    "\n",
-    "df.to_csv(\"test.csv\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Работа с данными - основные команды"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.info()\n",
-    "\n",
-    "print(df.describe().transpose())\n",
-    "\n",
-    "cleared_df = df.drop([\"Name\", \"Ticket\", \"Embarked\"], axis=1)\n",
-    "print(cleared_df.head())\n",
-    "print(cleared_df.tail())\n",
-    "\n",
-    "sorted_df = cleared_df.sort_values(by=\"Age\")\n",
-    "print(sorted_df.head())\n",
-    "print(sorted_df.tail())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Работа с данными - работа с элементами"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(df[\"Age\"])\n",
-    "\n",
-    "print(df.loc[100])\n",
-    "\n",
-    "print(df.loc[100, \"Name\"])\n",
-    "\n",
-    "print(df.loc[100:200, [\"Age\", \"Name\"]])\n",
-    "\n",
-    "print(df[0:3])\n",
-    "\n",
-    "print(df.iloc[0])\n",
-    "\n",
-    "print(df.iloc[3:5, 0:2])\n",
-    "\n",
-    "print(df.iloc[[3, 4], [0, 1]])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Работа с данными - отбор и группировка"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "s_values = df[\"Sex\"].unique()\n",
-    "print(s_values)\n",
-    "\n",
-    "s_total = 0\n",
-    "for s_value in s_values:\n",
-    "    count = df[df[\"Sex\"] == s_value].shape[0]\n",
-    "    s_total += count\n",
-    "    print(s_value, \"count =\", count)\n",
-    "print(\"Total count = \", s_total)\n",
-    "\n",
-    "print(df.groupby([\"Pclass\", \"Survived\"]).size().reset_index(name=\"Count\")) # type: ignore"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Визуализация - Исходные данные"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = df[[\"Pclass\", \"Survived\", \"Age\"]].copy()\n",
-    "data.dropna(subset=[\"Age\"], inplace=True)\n",
-    "print(data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Визуализация - Сводка пяти чисел\n",
-    "\n",
-    "<img src=\"assets/quantile.png\" width=\"400\" style=\"background-color: white\">"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def q1(x):\n",
-    "    return x.quantile(0.25)\n",
-    "\n",
-    "\n",
-    "# median = quantile(0.5)\n",
-    "def q2(x):\n",
-    "    return x.quantile(0.5)\n",
-    "\n",
-    "\n",
-    "def q3(x):\n",
-    "    return x.quantile(0.75)\n",
-    "\n",
-    "\n",
-    "def iqr(x):\n",
-    "    return q3(x) - q1(x)\n",
-    "\n",
-    "\n",
-    "def low_iqr(x):\n",
-    "    return max(0, q1(x) - 1.5 * iqr(x))\n",
-    "\n",
-    "\n",
-    "def high_iqr(x):\n",
-    "    return q3(x) + 1.5 * iqr(x)\n",
-    "\n",
-    "\n",
-    "quantiles = data[[\"Pclass\", \"Age\"]].groupby([\"Pclass\"]).aggregate([\"min\", q1, q2, \"median\", q3, \"max\"])\n",
-    "print(quantiles)\n",
-    "\n",
-    "iqrs = data[[\"Pclass\", \"Age\"]].groupby([\"Pclass\"]).aggregate([low_iqr, iqr, high_iqr])\n",
-    "print(iqrs)\n",
-    "\n",
-    "data.boxplot(column=\"Age\", by=\"Pclass\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Визуализация - Гистограмма"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data.plot.hist(column=[\"Age\"], bins=80)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Визуализация - Точечная диаграмма"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.plot.scatter(x=\"Age\", y=\"Sex\")\n",
-    "\n",
-    "df.plot.scatter(x=\"Pclass\", y=\"Age\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Визуализация - Столбчатая диаграмма"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot = data.groupby([\"Pclass\", \"Survived\"]).size().unstack().plot.bar(color=[\"pink\", \"green\"])\n",
-    "plot.legend([\"Not survived\", \"Survived\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Визуализация - Временные ряды"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datetime import datetime\n",
-    "import matplotlib.dates as md\n",
-    "\n",
-    "ts = pd.read_csv(\"data/dollar.csv\")\n",
-    "ts[\"date\"] = ts.apply(lambda row: datetime.strptime(row[\"my_date\"], \"%d.%m.%Y\"), axis=1)\n",
-    "ts.info()\n",
-    "\n",
-    "print(ts)\n",
-    "\n",
-    "plot = ts.plot.line(x=\"date\", y=\"my_value\")\n",
-    "plot.xaxis.set_major_locator(md.DayLocator(interval=10))\n",
-    "plot.xaxis.set_major_formatter(md.DateFormatter(\"%d.%m.%Y\"))\n",
-    "plot.tick_params(axis=\"x\", labelrotation=90)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
--- a/poetry.lock
+++ b/poetry.lock
@ -2972,4 +2972,4 @@ files = [
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.12"
-content-hash = "095ac6b7bcf84d775058af69a3c9539a60efb7e0608cedd9497f60d7e2349474"
+content-hash = "7cb1f1c1ce95a977a43e5da305526e7f30da76b24f32c32b4e38efc982e3ff51"
--- a/pyproject.toml
+++ b/pyproject.toml
@ -17,6 +17,9 @@ apiflask = "^2.2.0"
 flask-cors = "^5.0.0"


+[tool.poetry.group.dev.dependencies]
+ipykernel = "^6.29.5"
+
 [build-system]
 requires = ["poetry-core"]
 build-backend = "poetry.core.masonry.api"