Build Python programming code for data science basics

Lengkap Disiarkan 3 tahun lepas Dibayar semasa penghantaran
Lengkap Dibayar semasa penghantaran

• You choose the dataset! (proprietary data, or a public data set that has not been well-described. you should put in the effort to work with it in a way that has not already been done. If you don’t have ready project or data, we can choose from [login to view URL])

• It is beginner stage (basic programming)

• Time for completion 2 weeks

**You may offer an existing project that you can share and sell, but need to be genuine and unique**

Areas to be covered to get sense of project requirements:

1. regression vs classification

2. The modeling process: data splitting, model creation, resampling, bias-variance tradeoff and hyperparameter tuning, and model evaluation. Implement the tools to put these pieces together in a ML modeling workflow.

3. Feature engineering: Understand why and when feature (and target) engineering needs to be applied. Understand the different methods applied to numeric and categorical features

4. Linear regression, principal component regression, partial least squares

5. Logistic regression: Understand why we use logistic regression for classification problems, how to implement and assess model performance

6. Regularized regression

7. Multivariate adaptive regression splines: Introduce the concept of non-linearities. Understand how MARS models capture non-linear relationships, how to tune them and assess & interpret results.

8. K-nearest neighbors Understand how KNN models measure similarity and the primary tuning hyperparameter(s) involved. Demonstrate a new way visualizing model performance by using MNIST data.

9. Decision trees Understand the concept of decision trees, how they partition features and make predictions, and how the depth of the tree effects bias & variance. This builds the foundation for bagging, random forests and gradient boosting machines.

10. Bagging: Understand how bagging can be applied to decision tree models, why and when it works along with the primary drawback that remains. This builds the foundation for random forests.

11. Random forests Understand how random forests extends bagging, how it has great out-of-the-box performance and general tuning strategies.

12. Gradient boosted machines Understand how gradient boosting works, the different variants of gradient boosting machines, how to tune and interpret their performance.

13. Stacked models & Auto ML Understand the various approaches to stacking models and grid searches. Also understand the strengths and weaknesses of automated machine learning.

14. Develop a reproducible analytical workflow for a specific type of data that you feel is not well-serviced by existing packages in Python (optional)

15. Create a Python package that can be distributed to other users that can be used to address a specific set of analytical problems (optional)

16. You may Explore text using Natural Language Processing -NLP. (optional)

17. Develop an interactive web-interface to a specific workflow, for training, exploring or reporting results (optional)

18. Explore a specific algorithm in Data Science that we have not covered in the course by applying it to different data sets and describing e.g. its assumptions, uses, interpretation and limitations. (optional)

Analisis Statistik Data Analytics Sains Data Python

ID Projek: #28171449

Tentang projek

8 cadangan Projek jarak jauh Aktif 3 tahun lepas

Dianugerahkan kepada:

mohandayman

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