Metadata-Version: 2.1
Name: mb_scripts
Version: 0.1.0
Summary: Machine Learning scripts that will quicken the modelling and data analysis process
Home-page: https://github.com/SathyaKrishnan1211/mb_scripts
Author: Sathya Krishnan Suresh
Author-email: <satyakrishnan.s@pec.edu>
License: UNKNOWN
Description: ![2022-04-25 (3)](https://user-images.githubusercontent.com/86184014/165095198-1f90196a-f4c5-42f5-92ed-60850a8386d5.png)<br>
        Creator : Sathya Krishnan Suresh<br>
        ## DESCRIPTION
        This is a python package to quicken the modelling and data analysis process.<br><br>
        PyPi : https://pypi.org/project/mb-scripts/
        
        ## Version 0.1.0 additions
        1. **plot_pr_roc_curve**<br>
        This function is used to plot precision, recall and roc measures of an estimator 
        passed to it in separate subplots.
        2. **tt_with_sc**<br>
        You can now comfortably split the data into training and test sets and simultaneously
        scaler the numerical features in your data.
        3. **resnet**<br>
        RESNET-34 CNN image classification architecture has been added to the already existing typicall cnn
        architecture and lecun5 cnn architecture.
        4. **dnn_scripts**<br>
        The new module in ðŸ”¥mb_scriptsðŸ”¥ that will contain functions for building dense models
        with Tensorflow
        5. **metrics update**<br>
        A lot of regression metrics have been added along with multiclass precision metrics
        variations.
        6. **plotting update**<br>
        `plot_2` and `train_validation_curve_for_rf` have been added and they will be super useful for
        monitoring overfitting when you are training a RandomForestClassifier model
        
        
        ## Installing and using mb_scripts
        The package can be downloaded using `pip install mb-scripts==<latest_version>`<br><br>
        Latest version : **0.1.0**.<br><br>
        Once you have installed mb_scripts you can begin using it.<br><br> Here are some examples for using mb_scripts.<br><br>
        `train_validation_curve_for_rf` - used to monitor `RandomForestClassifier`'s overfitting
        ![2022-05-07](https://user-images.githubusercontent.com/86184014/167239436-a77b2773-072e-4b66-b4ab-2b48089c9606.png)<br><br>
        `plot_decision_boundary` is used to visually look at the decision boundary of classification functions<br><br>
        ![2022-04-25 (1)](https://user-images.githubusercontent.com/86184014/165075925-daa9cdf5-cbe0-41fe-85fa-39395d4cf027.png)<br><br>
        `classifiers_metrics` returns a dataframe that consists of precision, recall, accuracy_score and f1_score for all the classification models passed.<br><br>
        ![2022-04-25 (2)](https://user-images.githubusercontent.com/86184014/165077324-b64aeb9f-170e-4630-a17e-5a0a9174a79e.png)<br><br>
        
        I am writing scripts regularly so the versions will keep changing for the next one month. Stay tuned.
        
Keywords: python,mb_scripts,machine learning,data science,data analysis
Platform: UNKNOWN
Classifier: Development Status :: 1 - Planning
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Description-Content-Type: text/markdown
