취소 완료

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.

기술: Python, Machine Learning (ML), 알고리즘, MATLAB/Mathematica, 데이터 마이닝

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( 0건의 리뷰 ) Hyderabad, India

프로젝트 ID: #22232466

이 프로젝트의 입찰 현황은 다음과 같습니다. 입찰자: 31명, 평균 입찰가: ₹469 (1시간 기준)

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