Tradition machine learning project - prediction of probability of a binary outcome
Budget: £10 – £20 GBP
Hello everyone,
I am looking for a machine learning expert who is able to create a python (ipynb) ML model to predict the probability of an outcome (classification project)
I anticipate that ipynb to contain the following steps:
- Preprocessing - assessment of collinearity of features
- Preprocessing - Removal of outliers
- Preprocessing - feature extraction using PCA
- Running the following algorithms: logistic regression - SVM - decision tree - Xgboost and random forest.
- Calculate training accuracy of models
- Use gird search to tune hyperparameters
- Use K-fold cross validation. Calculate accuracy
- Model evaluation (e.g., AUC. F1 score, recall, precision, confusion matrix,...)
- Draw learning curve (training and validation curves)
- Plot feature importance graph
- Calculate probability of outcome by entering new values of the features
We anticipate the final model to be tuned to provide the highest possible accuracy with minimal concerns on overfitting
I am looking for a machine learning expert who is able to create a python (ipynb) ML model to predict the probability of an outcome (classification project)
I anticipate that ipynb to contain the following steps:
- Preprocessing - assessment of collinearity of features
- Preprocessing - Removal of outliers
- Preprocessing - feature extraction using PCA
- Running the following algorithms: logistic regression - SVM - decision tree - Xgboost and random forest.
- Calculate training accuracy of models
- Use gird search to tune hyperparameters
- Use K-fold cross validation. Calculate accuracy
- Model evaluation (e.g., AUC. F1 score, recall, precision, confusion matrix,...)
- Draw learning curve (training and validation curves)
- Plot feature importance graph
- Calculate probability of outcome by entering new values of the features
We anticipate the final model to be tuned to provide the highest possible accuracy with minimal concerns on overfitting