# Product Price Prediction - using online scrapped data [closed]

AIM: To Predict Price of products based on data that I have taken from other online stores. e.g Predict price of Samsung Galaxy S10, data will be from multiple online stores.

Problem: Which Machine learning Model should I use for this problem?(LSTM, RNN, CNN, Reinforcement Learning) What should be my parameters? Here are the one I have in my dataset.

1) Name

2) Price

3) Model

4) Brand

5) Year of Release

6) Description

7) Name of Online Store

8) New/old

9) Images of Product ( Up to 5 images)

## closed as too broad by Xi'an, whuber♦Mar 19 at 14:05

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First of all lets make sure that RNN and LSTM don't fit for this problem as they are used in sequential problems where the sequence of input data is important to decide the output and reshuffling input sequence will effect the output. and sequential models fit in problems like

1. Language translation
2. Speech recognition
3. Video Analysis
4. Time Series Data

Moreover, CNN-s are used with Images so it fit in problems like

1. Object Detection
2. Face Recognition
3. Images Classification

However, your problem seem to be too simple to add such complexity on it. deep learning models may consume a lot of time, CPU and memory in training and in this type of problems simple regression algorithms may fit.

Moreover, I believe that Images may be important in case that Item is used only and you want to know the item condition from it's photos.

But I think the right approach to take is to try to solve the problem by the simplest way first (Simplicity goes first). then try more complex ways to solve the problem.

about the example you addressed I think you can determine Samsung Glaxy S10 or any new item price by calculating the average of this item price from a representative sample of online sellers. using only this feature i think you solved 50% of your problem