Update README.md

README.md

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 license: apache-2.0
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-This is a case study about creditworthiness classification where I did the whole process of XXX, from the data cleansing part until the deployment of the model in an application simulated for the banks managers. To achieve this goal, I analyzed and prepared the dataset for machine learning models. The applied models are: Logistic Regression, Decision Tree Classifier, and Random Forest Classifier, which are available in Python's sklearn library. To optimize the workflow and model results, I applied a personalized pipeline for model application and GridSearchCV for parameter optimization. The app development was made using gradio app.
+This is a case study about creditworthiness classification where I did the whole process of building a creditworthiness classification model and app, from the data cleansing part until the deployment of the model in an application simulated for the banks managers. To achieve this goal, I analyzed and prepared the dataset for machine learning models. The applied models are: Logistic Regression, Decision Tree Classifier, and Random Forest Classifier, which are available in Python's sklearn library. To optimize the workflow and model results, I applied a personalized pipeline for model application and GridSearchCV for parameter optimization. The app development was made using gradio app.
 
 The data is uploaded in path: german_credit_risk/data/raw, but it was first obtained from Kaggle  and can be obtained [HERE](https://www.kaggle.com/datasets/mpwolke/cusersmarildownloadsgermancsv).