Saravanan666/pypi-org huggingface dataset:
Package: numeral-system-py
numeral-system-pyConvert from one numeric system to other in python.Supports following:roman numeralspositional numerals (like binary, arabic (decimal), hexadecimal and others)Example of usageRoman>>>importnumeral_system>>>numeral_system.roman.encode(7)'VII'>>>numeral_system.roman.decode('XXII')22Positional>>>importnumeral_system>>>numeral_system.positional.encode(42,2)'101010'>>>numeral_system.positional.encode(12,3)'110'>>>numeral_system.positional.decode(101,2)5>>>numeral_system.positional.decode('AF',16)175
Package: numerator
numeratorScript that automatically renames files, so they get equally zero left padded
names.UsageImagine you have this folder:PSC:\test>lsDirectory:C:\test

ModeLastWriteTimeLengthName
---------------------------
-a---16.12.202215:170file-090.jpg
-a---16.12.202215:170file-1.jpg
-a---16.12.202215:170file-100.jpg
-a---16.12.202215:170file-5.jpg
-a---16.12.202215:170file-80.jpg
-a---16.12.202215:170info.txtAnd you want all files named "file-XXX.jpg" (except for the "info.txt").Checking possible renames without actual changes:pipinstallnumeratorcdC:\test
numerator--dry-runRenaming6filestopadding3"C:\test"1.Planningtorename'file-1.jpg'to'file-001.jpg'2.Planningtorename'file-5.jpg'to'file-005.jpg'3.Planningtorename'file-80.jpg'to'file-080.jpg'Actual renaming operation:cdC:\test
numeratorRenaming6filestopadding3"C:\test"1.Renamed'file-1.jpg'to'file-001.jpg'2.Renamed'file-5.jpg'to'file-005.jpg'3.Renamed'file-80.jpg'to'file-080.jpg'Contents of the folder after execution:PSC:\test>lsDirectory:C:\test

ModeLastWriteTimeLengthName
---------------------------
-a---16.12.202215:170file-001.jpg
-a---16.12.202215:170file-005.jpg
-a---16.12.202215:170file-080.jpg
-a---16.12.202215:170file-090.jpg
-a---16.12.202215:170file-100.jpg
-a---16.12.202215:170info.txt
Package: numerbay
NumerBay Python APIProgrammatic interaction withnumerbay.ai- the Numerai community marketplace.If you encounter a problem or have suggestions, feel free to open an issue or post in#numerbayin RocketChat.Credit: code structure adapted from thenumerapirepoInstallationpip install --upgrade numerbayUsagePython moduleCheckout the NumerBayPython Client Tutorialsfor usage examples.Command line interfaceTo get started with the cli interface, let's take a look at the help page:$ numerbay --help
Usage: numerbay [OPTIONS] COMMAND [ARGS]...

  Wrapper around the NumerBay API

  Options:
    --help  Show this message and exit.

  Commands:
    Commands:
      account   Get all information about your account!
      download  Download artifact file.
      listings  Get all your listings!
      orders    Get all your orders!
      sales     Get all your sales!
      submit    Upload artifact from file.
      version   Installed numerbay version.Each command has it's own help page, for example:$ numerbay submit --help
Usage: numerbay submit [OPTIONS] PATH

  Upload artifact from file.

Options:
  --product_id INTEGER      NumerBay product ID
  --product_full_name TEXT  NumerBay product full name (e.g. numerai-
                            predictions-numerbay), used for resolving
                            product_id if product_id is not provided
  --order_id INTEGER        NumerBay order ID, used for encrypted per-order
                            artifact upload
  --help                    Show this message and exit.API ReferenceCheckout thedetailed APIdocs to learn about all available methods, parameters and returned values.
Package: numereval
A small library to reproduce the scores on numer.ai diagnistics dashboard.Installationpip install numerevalStructureNumerai main tournament evaluation metricsnumereval.numereval.evaluateA generic function to calculate basic per-era correlation stats with optional feature exposure and plotting.Useful for evaluating custom validation split from training data without MMC metrics and correlation with example predictions.from numereval.numereval import evaluate

evaluate(training_data, plot=True, feature_exposure=False)Correlations plotReturned metricsnumereval.numereval.diagnosticsTo reproduce the scores on diagnostics dashboard locally with optional plotting of per-era correlations.fromnumereval.numerevalimportdiagnosticsvalidation_data=tournament_data[tournament_data.data_type=="validation"]diagnostics(validation_data,plot=True,example_preds_loc="numerai_dataset_244\example_predictions.csv",)Validation plotReturned metricsSpecific validation erasspecify a list of eras in the formateras = ["era121", "era122", "era209"]validation_data=tournament_data[tournament_data.data_type=="validation"]eras=validation_data.era.unique()[11:-2]numereval.numereval.diagnostics(validation_data,plot=True,example_preds_loc="numerai_dataset_244\example_predictions.csv",eras=eras,)Validation plotReturned metricsNumerai Signals evaluation metricsNote: Since predictions are neutralized against Numerai's internal features before scoring, results fromnumereval.signalseval.run_analytics()do not represent exact diagnostics and live scores.importnumerevalfromnumereval.signalsevalimportrun_analytics,score_signals#after assigning predictionstrain_era_scores=train_data.groupby(train_data.index).apply(score_signals)test_era_scores=test_data.groupby(test_data.index).apply(score_signals)train_scores=run_analytics(train_era_scores,plot=False)test_scores=run_analytics(test_era_scores,plot=True)train_scorestest_scoresThanks toJason Rosenfeldfor allowing therun_analytics()to be integrated into the library.Docs will be updated soon!
Package: numerical-analysis-aman
VERSION 1.0.1Numerical AnalysisThis Module is Numerical analysis, area of mathematics and computer science that creates, analyzes, and implements algorithms for obtaining numerical solutions to problems involving continuous variables. Such problems arise throughout the natural sciences, social sciences, engineering, medicine, and business.New AddedNumerical-analysisVectorOfficial ReleasePypi,PepyHow to InstallPip install this module from your consolepipinstallnumerical-analysis-amanImport this module to your Work Space# Import In Python FilefromNumerical_Analysis_Amanimport<Module># =>> _MODULES_# -> Numerical_Algebra# -> Numerical_Analysis# -> Numerical_Integration# -> Numerical_Interpolation# Import allfromNumerical_Analysis_Amanimport*Explore (How It Works)This at present contain 4 parts You can explore them from Below and have some
idea about all methods and FunctionsNumerical IntegrationThis part contain interpolation.x=Numerical_Analysis_Aman.Numerical_Integration(lower,upper,function)x.Trapazoid(itration=2)Trapazoid Methodx.Simpson_13(itration=2)Simpson 1/3x.Simpson_38(itration=2)Simpson 3/8Numerical AnalysisThis part contain Integration Method having Three methodx = Numerical_Analysis_Aman.Numerical_Analysis(x_0,y_0,x_given,gap,function)x.Eular( itration = 4 )Eularx.EularModified( itration = 4 )EularModifiedx.RungaKutta( itration = 4 )RungaKuttaNumerical InterpolationThis part contain Analysis Method having Four methodx=Numerical_Analysis_Aman.Numerical_Interpolation(x_list,y_list,find_value)x.Langrangian()Langrangianx.Newton_Divided()Newton Divided Differencesx.Newton_Forward()Newton Forwardx.Newton_Backward()Newton BackwardNumerical AlgebraThis part contain Analysis Method having Three methodx=Numerical_Analysis_Aman.Numerical_Algebra(list_1,list_2,list_3)x.Jacobi(itration=6)Jacobix.Gauss_Seidel(itration=6)Gauss Seidelx.Gauss_Seidel_4(list_4,itration=6)Gauss Seidel for 4 variableUsage/ExamplesExample and sample for input and how to work on itimportNumerical_Analysis_Amanasnax=na.Numerical_Integration(2,7,"1/(5*x+3)")y=na.Numerical_Analysis(0,1,0.2,0.1,"((x**3)*(math.e**(-2*x))-(2*y))")z=na.Numerical_Interpolation([1891,1901,1911,1921,1931],[46,66,81,93,101],1925)w=na.Numerical_Algebra([10,1,-1,11.19],[1,10,1,28.08],[-1,1,10,35.61])# All of them are Initiated at once you can use them individualy as per requirement# default Itrations - 2print(x.Trapazoid())print(x.Simpson_13())print(x.Simpson_38())# default Itrations - 4print(y.Eular())print(y.EularModified())print(y.RungaKutta())print(z.Langrangian())print(z.Newton_Divided())print(z.Newton_Forward())print(z.Newton_Backward())# default Itrations - 6print(w.Jacobi())print(w.Gauss_Seidel())# needed Additional list for 4 variable# print(w.Gauss_Seidel_4(list_4))ContactAuthors@AmanKanojiyaIf any Issue Contact Me through Emailaman.kanojiya4203@gmail.comlicenseThis repository is licensed under the MIT license.
See LICENSE for details.
Package: numericalAnalysisTool
pyNumericalAnalysisToolKitit's a python module use to make numerical analysis on mathematical formulasHow to installpipinstallnumericalAnalysisToolexampleYou can find other example inexamples/directory.In this example I will take two function taking two paramter and returning one value.f\_add(a,b) = a + bf\_mult(a,b) = a.bSo you will have to define those functiondeff_add(a,b):returna+bdeff_mult(a,b):returna*bHow to use the tool kit :fromnumericalAnalysis.analysorimportAnalysoranalysor=Analysor()params_dict={"a":[i/100foriinrange(0,100+1)],# define range value for your parameter a"b":range(-20,20,3)# range value for parameter b}analysor.add_function(f_mult,params_dict)analysor.add_function(f_add,params_dict)analysor.compute()# it will compute product of every possible value for a and b gived beforeanalysor.draw_linear_2D_graph()# it will draw and save fig in "./graph" directoryIt will product in/example/graphdirectory all graph representing lineplot as the result of those function in function of every parameter.effect of parameter aeffect of parameter b
Package: numerical-derivative-checker
Numerical Derivative CheckerThis package contains a numericcal derivative checker that can be used to verify first or second order derivatives by
comparing them to the central finite difference approximation.The following combinations of input and output types are currently supportedDerivative orderOutput typescalarvectorscalarfirst / secondfirstInput typevectorfirst / secondfirstmatrixfirst---Sampling typesInhelper_functions.py, there are different sampling routines defined to randomize the function inputs.
The following samplers are currently implemented:ScalarSamplerVectorSamplerMatrixSamplerPosDefMatrixSamplerEach sampling routine has various parameters such as minimum/maximum values or dimensionality.ExamplesScalar input and output: f(x) = x**2from numerical_derivative_checker import check_derivative, ScalarSampler
check_derivative(sampler=ScalarSampler(n_samples=200),
                 fun=lambda x: np.sin(x),
                 grad=lambda x: np.cos(x),
                 hess=lambda x: -np.sin(x))Vector input and scalar output: f(x) = x^T A xfrom numerical_derivative_checker import check_derivative, VectorSampler
A = np.random.rand(5, 5)
check_derivative(sampler=VectorSampler(n=5),
                 fun=lambda x: x @ A @ x,
                 grad=lambda x: (A + A.T) @ x)Pos. def. matrix input and scalar output: f(X) = log(det(X))from numerical_derivative_checker import check_derivative, PosDefMatrixSampler
check_derivative(sampler=PosDefMatrixSampler(n=3),
                 fun=lambda x: np.linalg.det(x),
                 grad=lambda x: np.linalg.det(x) * np.linalg.inv(x),
                 tol=1e-3)seeexamples.pyfor more examples.
Package: numerical-methods
Numerical methodsImplementation of methods for solving mathematical problems in numerical formIntegralsLet's try calculate integral $\int_{-1}^{4} ! (2x^2-3) , \mathrm{d}x = \frac {85} 3$The whole code avialable innotebookin this repoAt first, we will find analytical (exact) solution:$pipinstallsympyfromsympyimportsymbols,integratex=symbols('x')f=(2*x**2-3)display(integrate(f,(x,-1,4)))$\frac{85}{3}$exact_solution=85/3print(f"exact solution ={exact_solution}")>>> exact solution = 28.333333333333332Now we will calculate it numerically by rectangle method with 10 rectangles:$I = \int_{a}^b f(x) \mathrm dx \approx \sum_{i=0}^{n-1}f(x_i)(x_{i+1}-x_i)$fromintegrateimportrectangle_methoddeff(x):return2*x**2-3integral=rectangle_method(-1,4,10)print("Approximate integral:",integral[0])print('Difference between exact and approximate solutions equals',abs(exact_solution-integral[0]))>>> Approximate integral: 28.125
>>> Difference between exact and approximate solutions equals 0.20833333333333215After increasing number of rectangles (from 10 to 100) difference between exact and approximate solutions is less significant:integral=rectangle_method(-1,4,100)print('Exact solution = ',exact_solution)print("Approximate integral:",integral[0])print(f'Difference between exact and approximate solutions equals{abs(exact_solution-integral[0]):.15f}')>>> Exact solution =  28.333333333333332
 >>> Approximate integral: 28.331249999999958
 >>> Difference between exact and approximate solutions equals 0.002083333333374to be continued in close times
Package: numericalmodel
Classes to set up and run a simple numerical model in PythonA graphical user interface to run aNumericalModelInstallThis package is onPyPi. To installnumericalmodel, runpip3install--usernumericalmodelIf you want the GUI too, have a look at thePrerequisitesand then installnumericalmodellike so:pip3install--user'numericalmodel[gui]'DocumentationYou can find detailed documentation of this packagehere on GitLab.
Package: numericalpoissongeometry
Numerical Poisson GeometryA Python module for (local) Poisson-Nijenhuis calculus on Poisson manifolds, with the following functions:num_bivector_fieldnum_bivector_to_matrixnum_poisson_bracketnum_hamiltonian_vfnum_sharp_morphismnum_coboundary_operatornum_modular_vfnum_curl_operatornum_one_forms_bracketnum_gauge_transformationnum_linear_normal_form_R3num_flaschka_ratiu_bivectorThis repository accompanies our paper'On Computational Poisson Geometry II: Numerical Methods'.MotivationThis project includes numerical methods that implementation parts of:Miguel Evangelista-Alvarado, José C. Ruíz Pantaleón & P. Suárez-Serrato,On Computational Poisson Geometry I: Symbolic Foundations,arXiv:1912.01746 [math.DG] (2019)🚀Bugs & ContributionsOur issue tracker is athttps://github.com/appliedgeometry/NumericalPoissonGeometry/issues. Please report any bugs that you find. Or, even better, if you are interested in our project you can fork the repository on GitHub and create a pull request.Licence 📄MIT licenceAuthors ✒️This work is developed and maintained by:José C. Ruíz Pantaleón-@jcrpantaPablo Suárez Serrato-@psuarezserratoMiguel Evangelista-Alvarado-@mevangelista-alvaradoThanks for citing our work if you use it! 🤓@misc{evangelistaalvarado2020computational,title={On Computational Poisson Geometry II: Numerical Methods},author={M. Evangelista-Alvarado and J. C. Ruíz-Pantaleón and P. Suárez-Serrato},year={2020},eprint={2010.09785},archivePrefix={arXiv},primaryClass={math.DG}}AcknowledgmentsThis work was partially supported by the grants CONACyT, “Programa para un Avance Global e Integrado de la Matemática Mexicana” CONACyT-FORDECYT 26566 and "Aprendizaje Geométrico Profundo" UNAM-DGAPA-PAPIIT-IN104819. JCRP wishes to also thank CONACyT for a postdoctoral fellowship held during the production of this work.
Package: numeric-methods
Numeric methodsNumeric methods is a package of tools for analyze functions via numeric methodsFeaturesFor now this project supports these features:One variable methods inone_variablemodule:half_method: half division method for equation root searchingInstallationFor installing this package you can usepip:py -m pip install numeric-methodsChanging languageThis project supports several languages. You can also add yours. For now supports English and Russian. You can set language by usingsettings.set_language:fromnumeric_methodsimportsettingssettings.set_language("en"or"EN"or"english"or"ENGLISH")# ^^^ Returns TruePlease note that the default language is English and errors have no translation.Example of usingHalf methodfromnumeric_methods.one_variableimporthalf_methodforlineinhalf_method(lambdax:x**5-2,1,2,0.001):print(line)# Prints vvv## (1, 1.0, 2.0, 1.5, 5.59375)# (2, 1.0, 1.5, 1.25, 1.0517578125)# (3, 1.0, 1.25, 1.125, -0.197967529296875)# (4, 1.125, 1.25, 1.1875, 0.3613920211791992)# (5, 1.125, 1.1875, 1.15625, 0.06661096215248108)# (6, 1.125, 1.15625, 1.140625, -0.06930162664502859)# (7, 1.140625, 1.15625, 1.1484375, -0.002269843505928293)# (8, 1.1484375, 1.15625, 1.15234375, 0.03193706909132743)# (9, 1.1484375, 1.15234375, 1.150390625, 0.014775536791518107)# 1.150390625
Package: numericnormalizer
numericnormalizerThis is a basic library used for NLP that can perform conversions between numbers in numerical format and alphabetical / character format.Installationpip install numericnormalizerUsageimporting the modulefromnumericnormalizerimportnormalizerConvert a number to a word (i.e. 5 -> 'five')normalizer.number_to_word(5,lang='en')>>"five"normalizer.number_to_word(5,lang='zh')>>"五"Convert a word to a number (i.e. 'five' -> 5)normalizer.word_to_number('five',lang='en')>>5normalizer.number_to_word('五',lang='zh')>>5Format numbers in a sentenceExample 1: default formattingnormalizer.format_sentence(sentence='What are the 6 principles of intercultural adaption?',lang='zh')>>"What are the six (6) principles of intercultural adaption?"Example 2: Custom Formattingnormalizer.format_sentence(sentence='I have 4 apples and five oranges.',lang='zh',formatting='{number}[{word}]',# custom formatting)>>"I have 4 [four] apples and 5 [five] oranges."Example 3: Number restrictingnormalizer.format_sentence(sentence='I have 4 apples and five oranges.',lang='zh',max_number=4# restrict the max_number)>>"I have four (4) apples and five oranges."Language SupportThe supported languages are from theAzure Language Detect List:Afrikaans (af)Albanian (sq)Amharic (am)Arabic (ar)Armenian (hy)Assamese (as)Azerbaijani (az)Bashkir (ba)Basque (eu)Belarusian (be)Bengali (bn)Bosnian (bs)Bulgarian (bg)Burmese (my)Catalan (ca)Central Khmer (km)Chinese (zh)Chinese Simplified (zh_chs)Chinese Traditional (zh_cht)Chuvash (cv)Corsican (co)Croatian (hr)Czech (cs)Danish (da)Dari (prs)Divehi (dv)Dutch (nl)English (en)Esperanto (eo)Estonian (et)Faroese (fo)Fijian (fj)Finnish (fi)French (fr)Galician (gl)Georgian (ka)German (de)Greek (el)Gujarati (gu)Haitian (ht)Hausa (ha)Hebrew (he)Hindi (hi)Hmong Daw (mww)Hungarian (hu)Icelandic (is)Igbo (ig)Indonesian (id)Inuktitut (iu)Irish (ga)Italian (it)Japanese (ja)Javanese (jv)Kannada (kn)Kazakh (kk)Kinyarwanda (rw)Kirghiz (ky)Korean (ko)Kurdish (ku)Lao (lo)Latin (la)Latvian (lv)Lithuanian (lt)Luxembourgish (lb)Macedonian (mk)Malagasy (mg)Malay (ms)Malayalam (ml)Maltese (mt)Maori (mi)Marathi (mr)Mongolian (mn)Nepali (ne)Norwegian (no)Norwegian Nynorsk (nn)Odia (or)Pasht (ps)Persian (fa)Polish (pl)Portuguese (pt)Punjabi (pa)Queretaro Otomi (otq)Romanian (ro)Russian (ru)Samoan (sm)Serbian (sr)Shona (sn)Sindhi (sd)Sinhala (si)Slovak (sk)Slovenian (sl)Somali (so)Spanish (es)Sundanese (su)Swahili (sw)Swedish (sv)Tagalog (tl)Tahitian (ty)Tajik (tg)Tamil (ta)Tatar (tt)Telugu (te)Thai (th)Tibetan (bo)Tigrinya (ti)Tongan (to)Turkish (tr)Turkmen (tk)Upper Sorbian (hsb)Uyghur (ug)Ukrainian (uk)Urdu (ur)Uzbek (uz)Vietnamese (vi)Welsh (cy)Xhosa (xh)Yiddish (yi)Yoruba (yo)Yucatec Maya (yua)Zulu (zu)However for theformat_sentencefeature, as this is an early release not all languages have been tested thoroughly. It is currently designed to only check languages that deal with spaces as it relies on regex word match notationNumber supportCurrently only support numbers 0 - 10. No negatives.
Package: numeric_notation_parser
InstallationWith poetrypoetryaddnumeric_notation_parserWith pippipinstallnumeric_notation_parserSupported formatsInput is marked as a string, output as a list of intInputOutput as list10,11,22[10, 11, 22]0-10[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]0-10/2[0, 2, 4, 6, 8, 10]10--10/3[10, 7, 4, 1, -2, -5, -8]Examplefromnumeric_notation_parserimportnotation_to_integer_generator
Package: numerics
= numerics =/python experiments in numerics + plotting/== Installation ==Becausenumericsis largely a bunch of experiments, it requires
several packages.  Some of themmaybe challenging to install.
Here’s how, as best I know.=== OSX ===For matplotlib:http://superuser.com/questions/242190/how-to-install-matplotlib-on-os-xYou may also have to manually refresh the font cache:cd ~/.matplotlib/
fc-list  # Should take a couple minutes.(From:http://stackoverflow.com/questions/17490444/import-matplotlib-pyplot-hangs)== Libraries ==The following libraries, listed by language, may be worth investigating.=== JavaScript ===http://metricsgraphicsjs.org/https://github.com/mikedewar/d3py: python -> d3 bridgeJeff Hammelhttp://k0s.org/hg/numerics
Package: numericvision
numericvisionA Python 3 package for detecting numericseven-segment displaysin images and runningperspective correctionon them usingOpenCV 4.detect_box_sequences()first applies a series of filters to the input image to reduce noise, bring
out primary contours and bridge gaps between digit segments (seeimages.apply_filters()). Next,
all resulting contours are analyzed by comparing their geometric properties to a set of criteria
which define what it means for a contour to resemble a seven-segment digit. Lots of assumptions are
being made here (seeconfig.cfg). A contour resembling a seven-segment digit is used to
instantiate aBoxobject. Several boxes lined up in a row make up a boxSequence. Sequences are
contained in aBag.In other words, raw contours serve as input for an object detection pipeline which produces results
in the form of a crawlable object tree.Installation$ brew install opencv$ pip install numericvisionUsing the packagefromnumericvisionimportdetect_box_sequencesbox_sequences=detect_box_sequences("image.jpg")Running from the command line$ python -m numericvision image.jpg
Package: numering
NumeringYet another number package.random_floatrandom float number in range of A and Brandom_elementrandom element from listsizeofsize of an elementExamplesGenerating random float numberimportnumeringa,b=1,2print(numering.random_float(a,b),decimals=3)# 1.736Selecting random element from listimportnumeringmy_list=[1,2,3,4,5]print(numering.random_element(my_list))# 3Getting size of element (list, tuple, dict)importnumeringmy_list=[1,2,3,4,5]print(numering.sizeof(mylist))# 4
Package: numeritos
NUMERITOSTe damos la bienvenida a la librería 'Numeritos'.Muchas gracias por tu interés 😊Te invitamos a probar nuestra librería, un proyecto global que quiere facilitarle la vida a l@s científic@s de datos a través de un conjunto de funciones originales para Python.Introducción:Este proyecto nace de la colaboración entre los alumnos del BootCamp de Data Science de The Bridge - Digital Talent Accelerator, de la promoción de junio del 2022.Hemos puesto en común funciones que cada uno ha ido desarrollando a lo largo del curso para sus proyectos individuales.Hemos logrado agrupar 54 funciones muy útiles para tareas relativas a:visualización de datosprocesado de datosMachine LearningEstos pedazos de código encapsulado nos han resultado muy útiles a lo largo de nuestra formación, y por eso queremos compartirlos con todas las personas del fascinante mundo de los datos 🔮Qué contiene esta librería:Te ofrecemos las siguientes funciones:Visualizaciónfeature_visualgrafico_goscattergen_diagram_cajapieplot_one_columnjoyplot_one_columnsubplotsgraf_displotTransformación de datostime_nowsustituye_textoextraer_con_regexeliminar_entre_parentesis_en_dfwhere_containsdrop_con_condiciondata_reportnumber_of_outliersradical_droppingread_data_bwread_data_colorread_datasustituir_outliersmuestra_nanacotar_valores_clasewrap_perspective_cv2train_samplerstring_replacerbasic_encodingclean_emojiMinMaxCorrDfTransTypesustituye_nan_modatrain_regressionclean_edadMachine Learningfuncion_lineal_regressionfuncion_metricas_errorfuncion_ridgefuncion_lassoVerror_modelofeature_importancenine_Regressor_ModelsPruebaModelosroot_mean_squared_errorEquipo:Laura BarredaJavier TenorioAntonio MoralesMario SabrásChristian Jerome BaderSara De VeraIrene GlezXinmeng YeQinhua LiangLuis Miguel ValverdeEnrique RubioTarik El HannachEnlace a Paquete PypiDependencias principalesLas librerías que hemos ido utilizando durante el desarrollo son:CV2imblearnjoypymatplotlibNumpyosPandasplotlypygamereSeabornSkimageSklearntime