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NTI-Final-Assignment Use flask(python) and shiny dashboard (R) to build simple user interface to see how choosing classification model may affect prediction accuracy, using Customer Churn Dataset.

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Name		Name	Last commit message	Last commit date
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Python		Python
R		R
README.md		README.md

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NTI-Final-Assignment

NTI-Final-Assignment Use flask(python) and shiny dashboard (R) to build simple user interface to see how choosing classification model may affect prediction accuracy, using Customer Churn Dataset.

I tried to handle imbalanced classes in dataset using OverSampling technique (SMOTE Algorithm, RandomOverSampler) from imbalanced-learn API

Customer Churn probability (Yes / No).

i have used:

Logistic Regression.
KNN.
SVM.
Random forest.
Decision Tree.
Naive Bayes.

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NTI-Final-Assignment Use flask(python) and shiny dashboard (R) to build simple user interface to see how choosing classification model may affect prediction accuracy, using Customer Churn Dataset.

python data-science machine-learning r machine-learning-algorithms jupyter-notebook data-analysis flask-api shinydashboard prediction-algorithm imbalanced-data smote oversampling classification-algorithims randomoversampler

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