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Data Modeling furthere.ipynb
Data Modeling furthere.ipynb
 
 
Final Clean .ipynb
Final Clean .ipynb
 
 
Final Clean for GBM .ipynb
Final Clean for GBM .ipynb
 
 
Forward Features Selection .ipynb
Forward Features Selection .ipynb
 
 
GBM Shubham Data tuning .ipynb
GBM Shubham Data tuning .ipynb
 
 
GBM on Merged 3 Oct cleaned .ipynb
GBM on Merged 3 Oct cleaned .ipynb
 
 
GBM on without merge data .ipynb
GBM on without merge data .ipynb
 
 
GBM pridiction .ipynb
GBM pridiction .ipynb
 
 
GBM tuning on Trainingmanual cleaned.ipynb
GBM tuning on Trainingmanual cleaned.ipynb
 
 
GBM with Baadror_additional _data .ipynb
GBM with Baadror_additional _data .ipynb
 
 
GBM.ipynb
GBM.ipynb
 
 
GBM_Shubham.ipynb
GBM_Shubham.ipynb
 
 
HeadFile .ipynb
HeadFile .ipynb
 
 
Missing Again .ipynb
Missing Again .ipynb
 
 
Outlier gbm .ipynb
Outlier gbm .ipynb
 
 
README.md
README.md
 
 
Z score removal of cleaned .ipynb
Z score removal of cleaned .ipynb
 
 
after processed data .ipynb
after processed data .ipynb
 
 
cleaned knn , regression .ipynb
cleaned knn , regression .ipynb
 
 
feature .ipynb
feature .ipynb
 
 
imbalance check .ipynb
imbalance check .ipynb
 
 
initial relation checking try 1.ipynb
initial relation checking try 1.ipynb
 
 
models fitting .ipynb
models fitting .ipynb
 
 
naive bayes classifier.ipynb
naive bayes classifier.ipynb
 
 
random forest .ipynb
random forest .ipynb
 
 

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Customer Credibility Analysis

Analysis is Done on the bank customer's data which have 80000 rows the problem statement was to solve that whether bank should give a customer credit card or not. In this code all the files are used in data processing and cleaning and modeling and parameter are here. The model used in this probelem which gav the best result was Gradient Boosting Machine Learning.

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Data Cleaning and modeling Approaches

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machine-learning-algorithms gradient-boosting-machine

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