This work basically includes data visualization practices.The purpose of this repo is to share my case studies.Pandas, NumPy, Matplotlib, and Seaborn libraries are used. I aim to constantly update the notebook file.
I'm parsing an e-commerce site known as a dataset.I store the data that i parse in PostgreSQL and also analyze it in pandas.Although the time series of my data is very weak at the moment, I aim to grow this database regularly in the future.The parsing process currently consists of the following features.
0 Product_No
1 Product_BreadCrum
2 Product_MinCatagory
3 Add_date
4 Product_Name
5 Product_Brand
6 Product_Free_Shipping
7 Product_Org_Price
8 Product_Dsc_Price
9 Product_Cart_Price
10 Product_insize
11 Product_outsize
12 Product_Ratings
13 Product_url
14 Product_img_url
15 id
To explain the Product_Org_Price, Product_Dsc_Price, Product_Cart_Price features;
As seen picture dsc_price is the main price and cannot be null.
1- Expanding the dataset regularly
2- Adding new features to the Dataset(e.g. marchent information and stock information)
3- To make meaningful analyzes with the growth of the data set and to follow the price changes