You can elect to uplift the return illustration or the section illustration or twain.
It is slight that you build most of the fickles in the cleaned grounds don’t keep pungent-muscular interrelation delay your target fickle. It does not average that you should percolate them all. The strength of Machine Learning is its faculty to nip out the predicting chattels from multiple features equal if the chattels is mean and link the chattels in the last illustration. Of plan, you should create the illustration absolute and solely emassociation features that contributes to the illustration. This averages that you force insufficiency to uplift multiple illustrations delay unanalogous combinations of features and eliminate the one delay the best enterprise. Luckily, it is irascible and quiet uniformly you keep the grounds all responsive. Keep all the features that force be suited in your groundsframe but solely emassociation the ones you shortness to use in any disuniteicular illustration in the set of predictors (X). Keep mark of the illustrations, associated predictor sets, and illustration enterprise domiciled on divert evaluation metrics. When uplifting the illustrations, try the following:
1. Try unanalogous illustrations. We literary a enumerate of illustrations in twain return and section. Try unanalogous ones to get a move of how they effect.
2. Try unanalogous hyper parameter values to get the best illustration you can, which averages to attenuate overfitting and lowerfitting to the elevatedest quantity.
3. If your keep a illustration that fit reasonable fit delay fitting enterprise, you probably don’t insufficiency to inspect elevate. But if you shortness to mend the illustration enterprise, you can go tail and revisal your features and foundation your predictors. We don’t keep anyover grounds to growth the grounds bulk so your can government out that possibility.
4. When disuniteitioning the grounds, use the failure slower of inoculation and trial (75 : 25) and set random_state = 0 so our illustrations are lower congruous state.
You can set Unit_Sold as your target fickle for return.
You can transform Unit_Sold into a binary fickle or a multi-class positive fickle using the cut() power of Pandas. As crave as you can reasonableify your slower points, any positive fickle generated would be sensitive.
What kind(s) of ML illustration you elect to uplift (return vs. section)? Why do you create this firmness (the valuable should foundation your profession concrete.)
· At the very elevated equalize, chat encircling the predictors chosen, hyper parameter segregation.
· If multiple illustrations keep been prepared to discover the best one, little chat encircling how you get the best one.
· If unanalogous illustrations are prepared (for illustration, straight return, ridge, KNN, etc.), perform-known all of them.
· Evaluate illustration enterprise using one or over metrics. Explain the rationale subsequently the valuable of metrics. Compare the illustrations delay the elect metrics. Provide your comments encircling illustration fit (overfit, lowerfit, reasonable fit?)
· If other steps keep been fascinated to boost the illustration enterprise, chat encircling them. For illustration, polynomial features are embodyd, etc.
What mysterious patterns or relationships keep been inspired? If you are to advice sellers to catalogue products on Wish, what would you state them? Create assured solely the essential discoverings, insights and recommendations are in the deep association of the PPT. Other slides could be in the epilogue of the PPT.
Obviously, you are scant by the grounds granted. If you keep all the instrument insufficiencyed, what can you do to create a rectify illustration? Get over grounds? Get over fickles? What joined fickles you shortness to keep to mend illustration enterprise, etc.
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