Package: mAdvisor
mAdvisor AutoML by MarlabsmAdvisor AutoML is an automated AI/ML solution from Marlabs that translates data into meaningful insights & predictions without any manual intervention. AutoML gives you the power of cognitive technologies like machine learning, machine reasoning, deep learning, natural language generation, natural language processing and expert rules systems with your limited knowledge of AI/ML, thereby enabling enterprises to identify revenue streams, enhance customer experience and productivity. This solution is designed for application and machine experts, so that machine learning models can be created with no help from a data scientist.  A 30-day trial license for mAdvisor AutoML® is issued on activation.The solution includes the following features:Ability to comprehend and monetize Big DataRapid time to insightsNo dependency on data scientists & analysts to create briefsRapid development of predictive appsExpandable and Scalable to the adoption of new use casesInstallationpip$pipinstallmAdvisorUsageA Licence Key is required to use this packageRegister yourself to activate the 30 days free trialConnect with Marlabs mAdvisor team to purchase a paid licence.User Registration and Plan SubscriptionSign Upimportrequestsbase_url='https://madvisor-dbc.marlabsai.com/automl'url=base_url+'/account/sign-up'data={'email':'<email id>','username':'<user name>','password':'<password>'}response=requests.post(url,data=data)ifresponse.status_code!=200:print('Failed response code {}'.format(response.status_code))print('Output result: {}'.format(response.json()))Subscribe to the 30 days trial planurl=base_url+'/subscription/subscribe-plan'data={'username':'<user name>','password':'<password>','subscription_type':'TRIAL','plan':'FREE'}response=requests.post(url,data=data)ifresponse.status_code!=200:print('Failed response code {}'.format(response.status_code))print('Output result: {}'.format(response.json()))Check Your Active Subscriptionsurl=base_url+'/subscription/active-plans'data={'username':'<user name>','password':'<password>'}response=requests.post(url,data=data)ifresponse.status_code!=200:print('Failed response code {}'.format(response.status_code))print('Output result: {}'.format(response.json()))Start Using the LibraryAutoML model trainingThis class is used to initiate AutoML training job, user has to use the slug value returned from here while doing prediction.frommAdvisorimporttrainmodel=train(train_data="<train dataset pass either path to the file or dataframe>",target="<Target Variable Name>",token="<Licence Key>")model_slug,_=model.fit()AutoML predictionTrain data prediction can be using this classfrommAdvisorimportscoremodel=score(test_data="<test dataset pass either path to the file or dataframe>",training_slug="<model slug value received from train output>",token="<Licence Key>")prediction_rules,predicted_data,_=model.fit()AutoML model Training and Prediction in a single goUsed to initiate automl job, both model training and scoring can be done using this class.frommAdvisorimportautomlmodel=automl(train_data="<train dataset pass either path to the file or dataframe>",test_data="<test dataset pass either path to the file or dataframe>",target="<Target Variable Name>",token="<Licence Key>")model_slug,prediction_rules,predicted_data,_=model.fit()Automated Data Preprocessing, Feature engineering and Feature Selection for train dataFeature engineering module will return two dataframes, one for linear algorithms & second one for tree based algorithms, and a slug value which is to be used while preparing test data.frommAdvisorimportAutoFEpreprocess=AutoFE(target="<Target Variable Name>",train_df="train dataframe to be used",token="<Licence Key>")linear_df,tree_df,fe_slug,_=preprocess.fit()Automated Data Preprocessing, Feature engineering and Feature Selection for test dataFeature engineering module will return two dataframes, one for linear algorithms and second one for tree based algorithms.frommAdvisorimportAutoFE_Testpreprocess_test=AutoFE_Test(test_df="<test dataframe to be used>",fe_slug="<Unique slug value received from train data feature engineering>",token="<Licence Key>")linear_df_test,tree_df_test,_=preprocess_test.fit()