Objective of the Project:
As of the course, the objective of the project is to learn and implement the to tools and techniques of Data Science on the Data Set, and to observe and critically analysis the data to make it useful for the stakeholders, for further development and progress of consumers.
Source of data:
Kaggle, US E - Commerce 2020 data
Brief description of the data:
Data Comprise of Work-hold stuff. • Having 3 main Categories in store, Furniture, Technology and Office Supplies, each having Sub-Category and Product Line respectively. Overall store has 17 Sub-Category, 9 in Office Supplies, 4 in Furniture, and 4 in Technology. And each Sub-Category has its product range. • The data Covers all the Region of US, Central, South, East and West, all the States, having all the Cities. • As it is a E-Store, there are four Shipping modes, First Class, Standard Class, Same Day, and Second Class. • And 3 types of Segments were there, one is Home office, Corporate and Consumers.
EDA Techniques:
• Load Dataset. • Fill empty data by similar field • Cleanse data to get executed • Use numpy , seaborn, matpltlib, sklearn, for loading manipulation and plotting • Convert Categorical data to numerical data to perform modeling • As data is unsupervised, so set Segment Feature as target variable.