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Binary classification problem for credit lead prediction. Used XGBoost, RandomForest and CatBoost model for prediction. Optimized hyperparameter using optuna.

github.com/dinabandhu50/credit-lead-prediction.git

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Name		Name	Last commit message	Last commit date
Latest commit History 2 Commits
.vscode		.vscode
catboost_info		catboost_info
input		input
models		models
notebooks		notebooks
output		output
src		src
.gitignore		.gitignore
README.md		README.md
env.yml		env.yml

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PROJECT CREDIT PREDICTION

How to use

open this folder in termonal
activate conda base environment
then run

conda env create -f env.yml
activate the conda env

conda activate all
the env setup is done here

kidney +

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Binary classification problem for credit lead prediction. Used XGBoost, RandomForest and CatBoost model for prediction. Optimized hyperparameter using optuna.

github.com/dinabandhu50/credit-lead-prediction.git

machine-learning random-forest random jupyter-notebook python3 xgboost classification catboost

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