Package: mlhybridx
ML Module that makes learning process smoothProject OverviewThis project focuses on the implementation of fundamental machine learning algorithms from scratch. The primary goal is to gain a deep understanding of how these algorithms work by coding them manually. By implementing algorithms like linear regression and logistic regression, we can explore their inner workings, mathematical foundations, and practical applications.Algorithms UsedLinear regressionIt is a basic algorithm for predicting a continuous target variable based on one or more input features. It involves fitting a linear relationship between the inputs and the target variable.we will more algorithms soon.InstallationYou can simply Install this Module just like another ModuleFirstly, make sure you have installed these libraries : [ numpy, pandas, scikit-learn, seaborn ]if you don't have these models no need to worry you can simply install it by :: pip install <Module name>pipinstallmlhybridxHere we go !. Now you can use it in your systems.Usage/ExamplesimporthybridfromhybridimportEasyRegressor"""If you have no idea about what can you do with datasetand how can you apply train_test_split, fitting in models etc for that youcan pass default parameter inside EasyRegressor onrunning that you will get the whole idea about any model."""# just run this and you get the whole idea about all processesEasyRegressor('default')# this takes random dataset from seaborn and tells about every process#                              output"""inside default*///////////////////////////////////////// ●▬▬▬▬◤ Dataset ◢▬▬▬▬● ///////////////////////////////////////////Date  Extent0     1980-01-01  14.2001     1980-01-03  14.3022     1980-01-05  14.4143     1980-01-07  14.5184     1980-01-09  14.594...          ...     ...13170 2019-12-27  12.72113171 2019-12-28  12.71213172 2019-12-29  12.78013173 2019-12-30  12.85813174 2019-12-31  12.889[13175 rows x 2 columns]//////////////////////////////// ●▬▬▬▬◤ split data ◢▬▬▬▬● ////////////////////////////////////////////////////// ●▬▬▬▬◤ Input data (X) ◢▬▬▬▬● //////////////////[[3.1553280e+17][3.1570560e+17][3.1587840e+17]...[1.5775776e+18][1.5776640e+18][1.5777504e+18]]////////////// ●▬▬▬▬◤ Output data (Y) ◢▬▬▬▬● ////////////////[14.2   14.302 14.414 ... 12.78  12.858 12.889]//////////////////////////////////// ●▬▬▬▬◤ trained data ◢▬▬▬▬● /////////////////////////////////////////////////////////////// ●▬▬▬▬◤ x_train ◢▬▬▬▬● //////////////////////////////[[5.588352e+17][4.779648e+17][7.509024e+17]...[7.919424e+17][6.342624e+17][6.913728e+17]]shape of x_train = (10540, 1)///////////////////////////////// ●▬▬▬▬◤ y_train ◢▬▬▬▬● /////////////////////////////[ 7.223 15.447  8.939 ... 15.259 15.277 11.725]shape of y_train = (10540,)///////////////////////////////// ●▬▬▬▬◤ x_test ◢▬▬▬▬● //////////////////////////////[[1.5468192e+18][7.9747200e+17][7.4908800e+17]...[1.5539040e+18][4.7243520e+17][8.6624640e+17]]shape of x_test = (2635, 1)///////////////////////////////// ●▬▬▬▬◤ y_test ◢▬▬▬▬● //////////////////////////////[13.282 14.686  6.9   ... 14.096 13.202 11.968]shape of y_test = (2635,)///////////////////////////////////////// ●▬▬▬▬◤ fitting into Liner Regression Models ◢▬▬▬▬● ////////////////////////////////////////////////////////////////////////////////////////// ●▬▬▬▬◤ By ols ◢▬▬▬▬● /////////////////////////////////////////////////m = [-1.92604083e-18]b = [13.19621591]Predicted value = [[10.21697898][11.66025228][11.75344184]...[10.20333336][12.28628643][11.52778998]]//////////////////////////////////////////// ●▬▬▬▬◤ By Multiple Regression ◢▬▬▬▬● ///////////////////////////////////////intercept_ = 13.196215914838561coef_ = [-1.92604083e-18]Predicted value = [[10.21697898][11.66025228][11.75344184]...[10.20333336][12.28628643][11.52778998]]///////////////////////////////////// ●▬▬▬▬◤ By Gradient Descent Regression ◢▬▬▬▬● //////////////////////////////////////For lr = 0.001 and epochs = 50intercept = 11.263270304178373coefficient = [-0.64567608]Predicted value = [[10.21697898][11.66025228][11.75344184]...[10.20333336][12.28628643][11.52778998]]"""# making an object# new = Hybrid('<name of your dataset in csv formate>')# suppose we have a dataset of wine.csvnew=EasyRegressor('wine.csv')# To see dataset in dataframenew.df()# output show below in image# if you want to give target column for which values you want to predict pass the target parameter inside split() method# new.split(target = 'pH')new.split()# by default it takes last column as a target column#                   output"""//////////////// ●▬▬▬▬◤ Input data (X) ◢▬▬▬▬● //////////////////[[ 7.4    0.7    0.    ...  3.51   0.56   9.4  ][ 7.8    0.88   0.    ...  3.2    0.68   9.8  ][ 7.8    0.76   0.04  ...  3.26   0.65   9.8  ]...[ 6.3    0.51   0.13  ...  3.42   0.75  11.   ][ 5.9    0.645  0.12  ...  3.57   0.71  10.2  ][ 6.     0.31   0.47  ...  3.39   0.66  11.   ]]////////////// ●▬▬▬▬◤ Output data (Y) ◢▬▬▬▬● ////////////////[5. 5. 5. ... 6. 5. 6.]"""# after splitting the data next part is the training part# before that we have to split train data and test datanew.train(test_size=0.3)# you can also pass test_size  and target in train , by default it runs on 0.2#                        output"""//////////////////////////////// ●▬▬▬▬◤ x_train ◢▬▬▬▬● //////////////////////////////[[ 7.3   0.39  0.31 ...  3.41  0.54  9.4 ][ 6.7   0.46  0.24 ...  3.39  0.6  10.6 ][ 7.5   0.57  0.02 ...  3.36  0.62 10.8 ]...[ 8.9   0.84  0.34 ...  3.12  0.48  9.1 ][12.8   0.3   0.74 ...  3.2   0.77 10.8 ][ 6.9   0.51  0.23 ...  3.4   0.84 11.2 ]]shape of x_train = (1119, 11)///////////////////////////////// ●▬▬▬▬◤ y_train ◢▬▬▬▬● /////////////////////////////[6. 6. 6. ... 6. 7. 6.]shape of y_train = (1119,)///////////////////////////////// ●▬▬▬▬◤ x_test ◢▬▬▬▬● //////////////////////////////[[ 7.2   0.63  0.   ...  3.37  0.58  9.  ][11.6   0.47  0.44 ...  3.38  0.86  9.9 ][ 7.7   0.96  0.2  ...  3.36  0.44 10.9 ]...[ 7.9   0.18  0.4  ...  3.33  0.93 11.3 ][ 9.9   0.53  0.57 ...  3.19  0.76 11.6 ][ 6.8   0.49  0.22 ...  3.41  0.83 11.3 ]]shape of x_test = (480, 11)///////////////////////////////// ●▬▬▬▬◤ y_test ◢▬▬▬▬● //////////////////////////////[6. 4. 5. ... 5. 7. 6.]shape of y_test = (480,)"""# Now Applying Model# we have three models in Linear Regression you can choose one of them according to your data set , i go for Multiple Linear Regression Model --> *** model_mlr ***new.model_mlr()#                                  output"""intercept = 25.141588069949456coefficient = [ 3.16451741e-02 -1.13297028e+00 -1.29162767e-01  2.98498912e-02-1.94941142e+00  2.85043937e-03 -2.85137633e-03 -2.10144417e+01-3.77024485e-01  9.04017724e-01  2.53185189e-01]"""# Next is to find the predicted values# predict method contains 2 params first is model and second is any value on which more values will be predicted second parameter is optionalnew.predict('mlr')# you can also pass second parameter# here prediction is on the basis of multple linear Regression model called ---> mlr#                                  output"""Predicted values = [[ 1.81051079e+02  1.47062302e+01 -1.29162767e-01 ...  8.43501273e+011.54861388e+01  2.26527478e+02][ 2.91674067e+02  1.06835761e+01  1.09331360e+01 ...  8.46015432e+012.25257835e+01  2.49154907e+02][ 1.93621873e+02  2.30029543e+01  4.89915485e+00 ...  8.40987114e+011.19663165e+01  2.74296495e+02]...[ 1.81051079e+02  1.14378238e+01  4.39632309e+00 ...  8.81213655e+011.90059611e+01  2.41612431e+02][ 1.93621873e+02  1.77232208e+01  6.65906601e+00 ...  8.10817209e+011.22177324e+01  2.34069954e+02][ 1.93621873e+02  1.65918493e+01  2.38499604e+00 ...  8.48529591e+011.32233959e+01  2.44126589e+02]]"""# Next part is to find the score for that we have score method which takes 2 params first is which type of score you want to find and second is model namenew.score('RMSE','mlr')#                                  output"""R2 Score = 0.3787468740719061'MSE = 0.6349626010421263'"""# If you have no idea about what can you do with dataset and how can you apply train, models etc for that you can pass default parameter inside EasyRegressor on running that you will get the whole idea about any model.LicenseThis project is licensed under the MIT License - see theLISCENCEfile for details.🔗 LinksDocumentation :MLHybridX · PyPiGithub Repo:MLHybridX-ModuleNow terminal version is also available !!check it outGithub Repo:mlhybridx-terminal-versionContributorDeepak YadavandSudheerLanguages used