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Machine learning in python

step 1: Fit and transform the data (normalise)

step 2: perform various feature selection techniques (Chi-Square, recursive feature elimination, Lasso)

step 3: apply the following ML techniques to the dataset

1. A 5 or 10 fold cross-validation to solve overfitting.

2. Multi-class logistic regression

3. Decision Tree

4. Random forest

5. KNN

6. SVM

7. Naive Bayes

8. Neural Network

9. Adaboost

10. voting classifier

11. Linear discriminant analysis

Hyperparameter tuning to solve overfitting and to improve accuracy.

generate the confusion matrix for each algorithm, print the accuracy, precision, recall, f1 score, kappa, print the confusion chart, or the heat map, display the ROC curve, print the PYCM report

Export a trained model (ensembled method) in python to make predictions on real-time data. And integrate it to a web application.

スキル: パイソン, Machine Learning (ML), アルゴリズム, Matlab and Mathematica, データマイニング

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