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End-to-end projects: customer churning prediction using the Random Forest Classifier Algorithm with 97% accuracy; performing pre-processing steps; EDA and Visulization fitting data into the algorithm; and hyper-parameter tuning to reduce TN and FN values to perform our model with new data. Finally, deploy the model using the Streamlit web app.
pankjsalunkhe/Data-Science-Project
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End-to-end projects: customer churning prediction using the Random Forest Classifier Algorithm with 97% accuracy; performing pre-processing steps; EDA and Visulization fitting data into the algorithm; and hyper-parameter tuning to reduce TN and FN values to perform our model with new data. Finally, deploy the model using the Streamlit web app.
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