The objective of this analysis was to use machine learning models to accurately predict credit risk.
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Updated
Jun 27, 2021 - Jupyter Notebook
The objective of this analysis was to use machine learning models to accurately predict credit risk.
Data Science Assessment from HLA
Creed is a dynamic repository where machine learning algorithms meet practical implementation, crafted through hands-on coding sessions in Google Colaboratory.
Built Random Forest and GBDT using XGBOOST model on Amazon fine food review dataset
515k Hotel reviews classifier with bidirectional lstm
Machine learning competition on kaggle
The Python code for solving a Regression problem for predicting optimum Health Insurance Cost for the Individual. Has through EDA and various Regressor models employed in the prediction. 1. Linear Regression 2. Lasso Regression 3. Elastic Net Regression 4. sklearn.tree.DecisionTreeRegressor 5. sklearn.ensemble.RandomForestRegressor 6. sklearn.ne…
Predict churning or not from the real-world data of a ridesharing app
I developed a model to predict the severity of earthquakes in Kavrepalanchok. The purpose is to obtain the best model and depth for the prediction and to emphasize the various steps needed to build such a model.
A face detection program in python using Viola-Jones algorithm.
Sohu's 2018 content recognition competition 1st solution(搜狐内容识别大赛第一名解决方案)
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