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This machine learning model was developed for "House Prices - Advanced Regression Techniques" competition in Kaggle by using several machine learning models such as Random Forest, XGBoost and LightGBM.
python
data-science
machine-learning
ai
random-forest
scikit-learn
jupyter-notebook
regression
prediction
artificial-intelligence
xgboost
lightgbm
machinelearning
ju
random-forest-regression
xgboost-regression
lightgbm-regressor
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Updated
Feb 5, 2022 - Jupyter Notebook
Customer churn, where customers leave a service provider for a competitor, poses significant challenges for telecom companies. This project develops a predictive model using a dataset of 100,000 records with 100 variables, aiming to identify likely churners and provide actionable insights to enhance retention strategies
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Updated
Jul 1, 2024 - Jupyter Notebook
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