پروژه یادگیری ماشین با پایتون (۱۳۱۴)

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شرح پروژه

سلام

This is an individual assignment. Provide APA references and citations for any external intellectual property used in your software design.
Using a single dataset of your choice (Kaggle, UCI, etc), apply each of the following algorithms to your dataset:
1) Linear regression (supervised), need to determine target dependent variable (e.g. continuous numerical type)
2) Random forest classification (supervised), need to determine target dependant variable (e.g. categorical type)
3) K-means clustering (unsupervised)
4) For each of the 3 algorithms above, apply PCA analysis to reduce the dimensionality of the dataset, and then run each of the 3 algorithms once more on the dimension-reduced dataset. In your Python code, measure the difference in time (likely a speedup) for the algorithm to run before and after PCA, and measure the difference in before and after PCA in: R2 or error for linear regression; accuracy or precision for random forest classification; and optimal cluster for k-means clustering.
In a Python script or a Jupyter Notebook, load the dataset, and then apply each of these algorithms using the libraries available.
Submit a .zip file containing your script(s) or notebook(s) to the dropbox for evaluation.
No report is required, but I will try to run your code "as is" after copying and unzipping to my computer.

من یک پروژه ماشین لرنینگ میخام با توجه به شرایط بالا

بر روی یک دیتاست انتخابی حتما از سایت kaggle باشه، به زبان پایتون باشه و تو jupyter notebook
فایل کد اجرای برنامه تو jupyter میخام
واینکه انالیز دیتاست و visualization هم حتما روی دیتا ست انجام بشه

حتما هم اون سه تا الگوریتم ذکر شده بالا تو پروژه روی دیتا ست انجام بشه

مهارت ها و تخصص های مورد نیاز

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