Traditional machine learning project with categorical outcome (xgboost and random forest)
Budget: £10 – £20 GBP
I would like an expert in machine learning algorithms using python to work with me on a jupyter notebook. The aim of the algorithm is create a prediction model. The notebook should include:
- Preprocessing of data (deleting outlier, replacing missing data with MICE, dimensionality reduction using PCA, testing collinearity)
- Assess feature importance
- Create an algorithm for a binary target (classification) including Xgboost and random forest
- Use K-cross validation to validate the model
- Assessment of learning curve
- Evaluation of the model using different scores
- Preprocessing of data (deleting outlier, replacing missing data with MICE, dimensionality reduction using PCA, testing collinearity)
- Assess feature importance
- Create an algorithm for a binary target (classification) including Xgboost and random forest
- Use K-cross validation to validate the model
- Assessment of learning curve
- Evaluation of the model using different scores