Saravanan666/pypi-org huggingface dataset:
Package: housing-library-5514
Median housing value predictionThe housing data can be downloaded fromhttps://raw.githubusercontent.com/ageron/handson-ml/master/. The script has codes to download the data. We have modelled the median house value on given housing data.The following techniques have been used:Linear regressionDecision TreeRandom ForestSteps performedWe prepare and clean the data. We check and impute for missing values.Features are generated and the variables are checked for correlation.Multiple sampling techinuqies are evaluated. The data set is split into train and test.All the above said modelling techniques are tried and evaluated. The final metric used to evaluate is mean squared error.To excute the scriptpython < scriptname.py >README File
Package: housing-library-5515
Median housing value predictionThe housing data can be downloaded fromhttps://github.com/ageron/handson-ml/blob/master/. The script has codes to download the data. We have
modelled the median house value on given housing data.The following techniques have been used:Linear regressionDecision TreeRandom ForestSteps performedWe prepare and clean the data. We check and impute for missing values.Features are generated and the variables are checked for correlation.Multiple sampling techinuqies are evaluated. The data set is split into train and test.All the above said modelling techniques are tried and evaluated. The final metric used to evaluate is mean squared error.
-performed the tests##sklearn pypi acount recreation
-added the functional tests and unit tests
-performed test_training and tets_installation tests in functional testings
-performed test_data_ingestion test in unit testingTo excute the scriptpython src/score.py##To Excute the distribution file
unzip dist/housing_library_5515-0.1-py3-none-any.whl -d wheel_contents
cd wheel_contents
python3 house_price_prediction/score.py##To activate the environment
conda activate mle-dev
##To export the environment
conda env export > environment.yml
##To import the environment
conda env create -f environment.yml
##LICENSE
This project is licensed under the MIT license - see the (LICENSE) file for details
Package: housing-library-5520
mle-training-Median housing value predictionThe housing data can be downloaded fromhttps://raw.githubusercontent.com/ageron/handson-ml/master/. The script has codes to download the data. We have modelled the median house value on given housing data.The following techniques have been used:Linear regressionDecision TreeRandom ForestSteps performedWe prepare and clean the data. We check and impute for missing values.Features are generated and the variables are checked for correlation.Multiple sampling techinuqies are evaluated. The data set is split into train and test.All the above said modelling techniques are tried and evaluated. The final metric used to evaluate is mean squared error.To excute the scriptpython < scriptname.py >Command to create Virtual Enviornemnt:conda --version
conda create --name mle-dev biopython
conda activate mle-devinstall the necessary librabries like numpy,pandas , matplotlib and scikit learnconda install numpy
conda install pandas
conda install matplotlibTo excute the scriptpython < scriptname.py >
python nonstandardcode.pyExporting the enviormentconda export --name MLE-training >env.yml
Package: housing-prediction
Median housing value predictionThe housing data can be downloaded fromhttps://raw.githubusercontent.com/ageron/handson-ml/master/. The script has codes to download the data. We have modelled the median house value on given housing data.The following techniques have been used:Linear regressionDecision TreeRandom ForestSteps performedWe prepare and clean the data. We check and impute for missing values.Features are generated and the variables are checked for correlation.Multiple sampling techinuqies are evaluated. The data set is split into train and test.All the above said modelling techniques are tried and evaluated. The final metric used to evaluate is mean squared error.Setup for developmentCreate conda environmentfoo@bar:~$condaenvcreate-fdeploy/conda/linux_cpu_py39.ymlfoo@bar:~$condaactivatemle-devPerform testTox have been configured with pytest to automate testing in virtualenv.foo@bar:~$toxTest a specific test file:foo@bar:~$tox---k<file_name>UsageInstall packageOption 1. From github:foo@bar:~$gitclonehttps://github.com/rishitoshsingh-ta/mle-training.gitfoo@bar:~$cdmle-trainingfoo@bar:~$pipinstall.Option 2. From PyPifoo@bar:~$pipinstallhousing-predictionTest installation:To test whether the package is successfully installed or not, start python session, and try to import housing. If it's imported successfully, then installation is completefoo@bar:~$python>>> import housingIt will install all the dependencies and the housing packageRun mlflow serverAs mlflow tracking is used in this project, first mlflow server needs to be started. In he command below, \<directory> can be file:///home/user/artifacts , where you want to store mlruns datafoo@bar:~$mlflowserver\--backend-store-urisqlite:///mlflow.db\--default-artifact-root<dricetory>\--host0.0.0.0\--port8889Run scriptsThere are two ways to run the scripts, as single command line tool and as python scripts.As command line toolfoo@bar:~$housingAs python scriptsfoo@bar:~$python-mhousing.ingest_datafoo@bar:~$python-mhousing.trainfoo@bar:~$python-mhousing.scoreYou can also access pass arguments, to find all available arguments:foo@bar:~$housing--helpfoo@bar:~$python-mhousing.ingest_data--helpfoo@bar:~$python-mhousing.trainfoo@bar:~$python-mhousing.scoreDefault argumentsThe defaults argument values are located in a.cfgfile located in:/path/to/env/lib/python3.9/site-packages/housing-prediction/housing.cfg. The defaults can be changes as per user preferences.
Package: housing-prediction-0.4
Median housing value predictionThe housing data can be downloaded fromhttps://raw.githubusercontent.com/ageron/handson-ml/master/. The script has codes to download the data. We have modelled the median house value on given housing data.The following techniques have been used:Linear regressionDecision TreeRandom ForestSteps performedWe prepare and clean the data. We check and impute for missing values.Features are generated and the variables are checked for correlation.Multiple sampling techinuqies are evaluated. The data set is split into train and test.All the above said modelling techniques are tried and evaluated. The final metric used to evaluate is mean squared error.Setup for developmentCreate conda environmentconda env create -f env.ymlconda activate <env_name>Perform testTox have been configured with pytest to automate testing in virtualenv.toxTest a specific test file:tox -- -k <file_name>UsageInstall packageOption 1. From github:git clone https://github.com/TejaML/mle-training.gitcd mle-trainingpip install .Option 2. From PyPipip install housing-predictionTest installation:To test whether the package is successfully installed or not, start python session, and try to import housing. If it's imported successfully, then installation is completepython>>> import housingIt will install all the dependencies and the housing packageRun scriptsThere are two ways to run the scripts, as single command line tool and as python scripts.As command line toolhousingAs python scriptspython -m housing.ingest_datapython -m housing.trainpython -m housing.scoreYou can also access pass arguments, to find all available arguments:housing --helppython -m housing.ingest_data --helppython -m housing.trainpython -m housing.score
Package: housing-prediction-devaraj-saravana
Median housing value predictionThe housing data can be downloaded fromhttps://raw.githubusercontent.com/ageron/handson-ml/master/. The script has codes to download the data. We have modelled the median house value on given housing data.The following techniques have been used:Linear regressionDecision TreeRandom Forest with RandomizedSearchCVRandom Forest with GridSearchCVSteps performedWe prepare and clean the data. We check and impute for missing values.Features are generated and the variables are checked for correlation.Multiple sampling techinuqies are evaluated. The data set is split into train and test.All the above said modelling techniques are tried and evaluated.Mean squared error, Root mean squaerd error, Mean absolute error metrics are used to evaluate the modelTo excute the script'sThere are three scripts need to run for evaluating the model$ python ingest_data.py -p rawyou can run this script with specifying where you want to place the downloaded data and also with default arguments$ python train.py -x housing_prepared.csv -y housing_labes.csvyou can run this script with specifying dependent and independent variables and also with no argument passed$ python score.py -m final_model.pkl -d test_set.csvyou can run this script with specifying which ML model want to use and with what dataset to score metrics
Package: housing-predictor-devaraj-saravana
Median housing value predictionThe housing data can be downloaded fromhttps://raw.githubusercontent.com/ageron/handson-ml/master/. The script has codes to download the data. We have modelled the median house value on given housing data.The following techniques have been used:Linear regressionDecision TreeRandom Forest with RandomizedSearchCVRandom Forest with GridSearchCVSteps performedWe prepare and clean the data. We check and impute for missing values.Features are generated and the variables are checked for correlation.Multiple sampling techinuqies are evaluated. The data set is split into train and test.All the above said modelling techniques are tried and evaluated.Mean squared error, Root mean squaerd error, Mean absolute error metrics are used to evaluate the modelTo excute the script'sThere are three scripts need to run for evaluating the model$ python ingest_data.py -p rawyou can run this script with specifying where you want to place the downloaded data and also with default arguments$ python train.py -x housing_prepared.csv -y housing_labes.csvyou can run this script with specifying dependent and independent variables and also with no argument passed$ python score.py -m final_model.pkl -d test_set.csvyou can run this script with specifying which ML model want to use and with what dataset to score metrics
Package: housing-pred-mle
Median housing value predictionThe housing data can be downloaded fromhttps://raw.githubusercontent.com/ageron/handson-ml/master/. The script has codes to download the data. We have modelled the median house value on given housing data.To set up the projectClone the repository.Create and activate virtual python development environment. On terminal, runInstall the packageVerify correct installationGenerate Sphinx DocumentationThe following techniques have been used:Linear regressionDecision TreeRandom ForestSteps performedWe prepare and clean the data. We check and impute for missing values.Features are generated and the variables are checked for correlation.Multiple sampling techinuqies are evaluated. The data set is split into train and test.All the above said modelling techniques are tried and evaluated. The final metric used to evaluate is mean squared error.
Package: housing-price-pred
Welcome to housing_price_pred's documentation!The housing data can be downloaded fromhttps://raw.githubusercontent.com/ageron/handson-ml/master/. The script has codes to download the data. We have modelled the median house value on given housing data.The following techniques have been used:Linear regressionDecision TreeRandom Forest - Both Randomized Search and Grid Search has been done for Hyperparameter Tuning.The trained models are saved in a specified directory from where they can be used to check the performance on the test set.Steps performedWe prepare and clean the data. We check and impute for missing values.Features are generated and the variables are checked for correlation.Multiple sampling techinuqies are evaluated. The data set is split into train and test.All the above said modelling techniques are tried and evaluated. The final metric used to evaluate is mean squared error.Install the packageInstall the package using python3 -mpython3 -m pip install housing_price_predUsageFrom Command Prompt typepython3to start PythonTo check availability inside python typehelp("modules"). The package should show up in the list.Package majorly contains 3 modules, ingest_data, train and score. Please note the train_data function from train module does not return any object, it simply trains the model on pre-processed data and stores the trained models as pickles inside specified directory. Incase returns are needed, Please raise and issue and this can be fixed.Import the modules from the packagefrom housing_price_pred import ingest_data,train,score.Functions inside these modules can be used with appropriate arguments.For more information on the functions, runhelp(function_name).Here is an usage example:
::from housing_price_pred import ingest_data,train,score
housing, strat_train_set, strat_test_set = download_data(housing_url, housing_path)
train_data(input_folder=args.input_path,processed_folder,pickle_path)
lr_predictions, tr_predictions, rnd_forest_predictions, grd_forest_predictions = score_models(processed_folder=\
                                                                                              pickle_path,\
                                                                                              output_path
                                                                                              )It is recommended to run train_data and score_models with default parameters if ingest_data is run with default parameters.For Contributors and DevelopmentFork the repo herehttps://github.com/sibashisc/mle-training/tree/fix/9%2Fml-workflowCreate a dev environment using the .yml fileconda env create -f env.ymlActivate environmentconda activate mle-devTo excute the scriptpython <scriptname.py>Each script ingest_data.py, train.py and score.py can take user arguments. For more information runpython3 <scriptname.py> --helpLicenseMIT License
Package: housing-price-prediction-2022
Median housing value predictionThe housing data can be downloaded fromhttps://raw.githubusercontent.com/ageron/handson-ml/master/. The script has codes to download the data. We have modelled the median house value on given housing data.The following techniques have been used:Linear regressionDecision TreeRandom Forest with RandomizedSearchCVRandom Forest with GridSearchCVSteps performedWe prepare and clean the data. We check and impute for missing values.Features are generated and the variables are checked for correlation.Multiple sampling techinuqies are evaluated. The data set is split into train and test.All the above said modelling techniques are tried and evaluated.Mean squared error, Root mean squaerd error, Mean absolute error metrics are used to evaluate the modelTo excute the script'sThere are three scripts need to run for evaluating the model$ python ingest_data.py -p rawyou can run this script with specifying where you want to place the downloaded data and also with default arguments$ python train.py -x housing_prepared.csv -y housing_labes.csvyou can run this script with specifying dependent and independent variables and also with no argument passed$ python score.py -m final_model.pkl -d test_set.csvyou can run this script with specifying which ML model want to use and with what dataset to score metrics
Package: housing-price-prediction-nitish
Median housing value predictionThe housing data can be downloaded fromhttps://raw.githubusercontent.com/ageron/handson-ml/master/. The script has codes to download the data. We have modelled the median house value on given housing data.The following techniques have been used:Linear regressionDecision TreeRandom ForestSteps performedWe prepare and clean the data. We check and impute for missing values.Features are generated and the variables are checked for correlation.Multiple sampling techinuqies are evaluated. The data set is split into train and test.All the above said modelling techniques are tried and evaluated. The final metric used to evaluate is mean squared error.To excute the scriptpython < scriptname.py >
Package: housing-price-predictor-devaraj-saravana
Median housing value predictionThe housing data can be downloaded fromhttps://raw.githubusercontent.com/ageron/handson-ml/master/. The script has codes to download the data. We have modelled the median house value on given housing data.The following techniques have been used:Linear regressionDecision TreeRandom Forest with RandomizedSearchCVRandom Forest with GridSearchCVSteps performedWe prepare and clean the data. We check and impute for missing values.Features are generated and the variables are checked for correlation.Multiple sampling techinuqies are evaluated. The data set is split into train and test.All the above said modelling techniques are tried and evaluated.Mean squared error, Root mean squaerd error, Mean absolute error metrics are used to evaluate the modelTo excute the script'sThere are three scripts need to run for evaluating the model$ python ingest_data.py -p rawyou can run this script with specifying where you want to place the downloaded data and also with default arguments$ python train.py -x housing_prepared.csv -y housing_labes.csvyou can run this script with specifying dependent and independent variables and also with no argument passed$ python score.py -m final_model.pkl -d test_set.csvyou can run this script with specifying which ML model want to use and with what dataset to score metrics
Package: housing-tiger-2022
Median housing value predictionThe housing data can be downloaded fromhttps://raw.githubusercontent.com/ageron/handson-ml/master/. The script has codes to download the data. We have modelled the median house value on given housing data.The following techniques have been used:Linear regressionDecision TreeRandom Forest with RandomizedSearchCVRandom Forest with GridSearchCVSteps performedWe prepare and clean the data. We check and impute for missing values.Features are generated and the variables are checked for correlation.Multiple sampling techinuqies are evaluated. The data set is split into train and test.All the above said modelling techniques are tried and evaluated.Mean squared error, Root mean squaerd error, Mean absolute error metrics are used to evaluate the modelTo excute the script'sThere are three scripts need to run for evaluating the model$ python ingest_data.py -p rawyou can run this script with specifying where you want to place the downloaded data and also with default arguments$ python train.py -x housing_prepared.csv -y housing_labes.csvyou can run this script with specifying dependent and independent variables and also with no argument passed$ python score.py -m final_model.pkl -d test_set.csvyou can run this script with specifying which ML model want to use and with what dataset to score metrics
Package: housing-traning-5519
mle-trainingMedian housing value predictionThe housing data can be downloaded fromhttps://raw.githubusercontent.com/ageron/handson-ml/master/. The script has codes to download the data. We have modelled the median house value on given housing data.The following techniques have been used:Linear regressionDecision TreeRandom ForestSteps performedWe prepare and clean the data. We check and impute for missing values.Features are generated and the variables are checked for correlation.Multiple sampling techinuqies are evaluated. The data set is split into train and test.All the above said modelling techniques are tried and evaluated. The final metric used to evaluate is mean squared error.To excute the scriptpython < scriptname.py >Command to create Virtual Enviornemnt:conda --version
conda create --name mle-dev biopython
conda activate mle-devinstall the necessary librabries like numpy,pandas , matplotlib and scikit learnconda install numpy
conda install pandas
conda install matplotlibTo excute the scriptpython < scriptname.py >
python nonstandardcode.pyExporting the enviormentconda export --name MLE-training >env.yml
Package: houston-client
HoustonPython Client forHouston, an open source tool for building low cost, scalable, platform-agnostic data pipelines.
Teams can share data processes as microservices using a simple API.Documentation:github.com/datasparq-ai/houston/docsInstallationThis client can be installed via pip:pipinstallhouston-clientIf you're running Houston on Google Cloud Platform you will need the 'gcp' plugin:pipinstall"houston-client[gcp]"UsageTo get started in under 15 minutes, see the quickstart repo:https://github.com/datasparq-intelligent-products/houston-quickstart-pythonThis client has the following uses:Saving, updating, and deleting plans: seesave command,delete commandStarting missions: seestart commandCreating Houston microservices:With Google Cloud Functions, seeCloud Function Service Decorator,Google Cloud, andPython Quickstart GCPWith any service: seehttps://github.com/datasparq-ai/houston/blob/main/docs/services.md, andservice.pyFull documentation is available atgithub.com/datasparq-ai/houstonRequirementsPython >= 3.7
Package: houtu
houtuOffline fast reverse geocoding. Named after the Chinese Goddess of the Earth Houtu.Various implementations are included.Naive computation of haversine distance matrix. Coordinates are not converted.Using ball-tree with haversine metric. Coordinates are not converted.Naive computation of euclidic distance matrix. Coordinates are converted from geodetic to ECEF and back.Using kd-tree with euclidic metric. Coordinates are converted from geodetic to ECEF and back.Installrequires Python 3.8+runpip install houtuUseimportnumpyasnpfromhoutuimportReverseGeocoderg=ReverseGeocode()arr=np.array([[25.04776,121.53185],# taipei[48.13743,11.57549],# munich],dtype=np.float32)coords,distances,cities=rg.query(arr,k=2,form="degrees")print(cities)Output[[
  City(name='Taipei', feature_class='P', feature_code='PPLC', country_code='TW', admin1_code='04', admin2_code='TPE', admin3_code='', admin4_code='', population=7871900, elevation=None, timezone='Asia/Taipei'),
  City(name='Neihu', feature_class='P', feature_code='PPL', country_code='TW', admin1_code='04', admin2_code='TPE', admin3_code='A14', admin4_code='63000100010', population=271594, elevation=None, timezone='Asia/Taipei')
], [
  City(name='Munich', feature_class='P', feature_code='PPLA', country_code='DE', admin1_code='02', admin2_code='091', admin3_code='09162', admin4_code='09162000', population=1260391, elevation=None, timezone='Europe/Berlin'),
  City(name='Bogenhausen', feature_class='P', feature_code='PPLX', country_code='DE', admin1_code='02', admin2_code='091', admin3_code='09162', admin4_code='09162000', population=77542, elevation=None, timezone='Europe/Berlin')
]]BenchmarkBatch of 2classtime/sReverseGeocodeBruteHaversine35.674ReverseGeocodeBallHaversine0.192ReverseGeocodeBruteEuclidic22.110ReverseGeocodeKdLearn0.392ReverseGeocodeKdScipy0.317Batch of 100classtime/sReverseGeocodeBruteHaversine193.119ReverseGeocodeBallHaversine0.918ReverseGeocodeBruteEuclidic86.881ReverseGeocodeKdLearn0.122ReverseGeocodeKdScipy0.095Optimizevincenty haversine
mean_absolute_error IUGG radius of sphere of same volume (R3) 897.7743386541117
mean_relative_error Spherical Earth Approx. of Radius (RE) 0.0018409171841267883
geodesic haversine
mean_absolute_error IUGG radius of sphere of same volume (R3) 897.7743386541117
mean_relative_error Spherical Earth Approx. of Radius (RE) 0.0018409171841267883
vincenty euclidic {'mean_absolute_error': 976.6828669488417, 'mean_relative_error': 0.0006505034968118167}
geodesic euclidic {'mean_absolute_error': 976.628990753796, 'mean_relative_error': 0.0006465253527447903}Developmentrun testspython -m unittest discover -s testsSourcesCities database filehoutu/data/cities1000.txt.xzis downloaded and recompressed fromhttp://download.geonames.org/export/dump/cities1000.zip(Creative Commons Attribution 4.0 License).GeoNames issuesThere are about 50 pairs of cities which have the exact same coordinates, for exampleFajã de BaixoandRosto de Cão.