GitHub - pankjsalunkhe/Data-Science-Project: 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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Name		Name	Last commit message	Last commit date
Latest commit History 6 Commits
Business_Requirement (1).docx		Business_Requirement (1).docx
Churn.csv		Churn.csv
Churn_Without_Outliers		Churn_Without_Outliers
Deployment (1).ipynb		Deployment (1).ipynb
Deployment.ipynb		Deployment.ipynb
Deployment.py		Deployment.py
Project_Churn_Final.ipynb		Project_Churn_Final.ipynb
Read Me.docx		Read Me.docx
Telecommunications_Churn Prediction.pptx		Telecommunications_Churn Prediction.pptx
data_profile_report.html		data_profile_report.html

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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.

accuracy cleaning-data deployment eda feature-engineering feature-selection model modelselection random-forest readingdatasets streamlit visualization webpage

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