Exploratory Data Analysis (EDA): Data Science using Python

Tool Used: Jupiter Notebook, Python ( Pandas, Numpy, Matplotlib, Seaborn )

Objective: Delve into the intricate dynamics of consumer behaviour and pricing strategies within the retail industry. We aim to analyze how pricing decisions impact consumer perceptions of value, explore the role of discounts in driving purchasing decisions, and highlight the significance of price ranges in attracting diverse customer segments. Through this exploration, we seek to provide actionable insights for retail businesses looking to optimize their pricing strategies and enhance customer engagement.

Projects Summary: When a product has a higher starting price, like ₹3000, it usually gets bigger discounts, like 30% or 25% off. People think they're getting a better deal with these larger discounts, which affects how they view the product's value compared to its cost. It's worth noting that prices between ₹0-1500 are particularly attractive to customers and can draw in more buyers.

GitHub: https://github.com/ntnr737/EDA-Using-Python/tree/main

YouTube (Coding Part ): https://youtu.be/hiydHbX9ti4?feature=shared

See below attached PPT slides: