Welcome to the Diwali Sales Analysis Using Python project! In the realm of data science, Exploratory Data Analysis (EDA) stands as a pivotal element, often dictating the trajectory of analysis. This repository serves as an illustrative guide to the techniques and methodologies employed in EDA, powered by Python.
The focal point of this project revolves around the Diwali Sales dataset, sourced from Kaggle. This dataset serves as an ideal playground for delving into EDA, a crucial step toward unlocking the fascinating domain of data science.
- Data Cleaning and Manipulation: A meticulous process of data refinement sets the stage for meaningful analysis.
- Exploratory Data Analysis (EDA): Employing the prowess of Pandas, NumPy, Matplotlib, and Seaborn, we unearth hidden patterns and trends.
- Enhancing Customer Experience: Unveiling potential customers across states, professions, genders, and age groups, thereby elevating user satisfaction.
- Optimizing Sales: Identifying best-selling product categories and items, enabling effective inventory management to meet market demands.
The analysis reveals that:
- Demographic Preference:
Married women aged 26-35 exhibit significant interest in purchasing. Predominantly from Uttar Pradesh, Maharashtra, and Karnataka.
- Occupational Inclination:
Professionals in IT, Healthcare, and Aviation sectors are prominent buyers.
- Product Preference:
Strong inclination towards Food, Clothing, and Electronics categories. These findings underscore the importance of targeted strategies to enhance customer engagement and optimize sales across different demographics.
The project demonstrates how data-driven insights can shape customer experience and enhance sales strategies. For detailed information, feel free to explore the code and findings within this repository.
Connect with me on LinkedIn: Shivam Verma
For inquiries, reach out via email: [email protected]
Thank you for exploring this project!
Note: The images, insights, and analyses presented in this project are based on the Diwali Sales dataset, and the conclusions drawn are rooted in the dataset's context and scope.