Skip to content

Embark on a data-driven journey into e-commerce customer behavior using Python. Uncover captivating insights on spending trends, age group dynamics, and regional nuances. Elevate decision-making with compelling visualizations, detailed reports, and interactive dashboards. Harness the power of Python's Pandas, NumPy, and sophisticated data visualiza

Notifications You must be signed in to change notification settings

priyanshusuyal/Data-Visualisation-in-Python-using-Matplotlib-and-Seaborn

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

3 Commits
 
 
 
 
 
 

Repository files navigation

Data-Visualisation-in-Python-using-Matplotlib-and-Seaborn

Overview: This project focuses on acquiring actionable insights into customer behavior within an e-commerce platform. By harnessing diverse data dimensions, including demographics, purchasing patterns, and regional specifics, the analysis seeks to provide valuable inputs for strategic decision-making within the business.

Objectives:

Gender-Based Spending Patterns:

Conduct a nuanced analysis of spending by male and female customers. Derive statistical measures, assess shopping frequency, and identify significant trends. Age Group Expenditure:

Investigate the contributions of distinct age groups to overall sales. Visualize the distribution of expenditures across various age demographics. Regional Shopping Analysis:

Explore shopping patterns within geographic regions in India. Identify regional preferences, peak periods, and potential market opportunities. Marital Status and Shopping Behavior:

Scrutinize the influence of marital status on customer spending. Evaluate the impact on shopping frequency and the nature of purchases. Product Category Preferences:

Discern popular product categories among customers. Analyze the distribution of expenditures across different product types. Occupation and Spending Habits:

Investigate correlations between customer occupations and spending habits. Identify patterns based on various professional backgrounds. Customer Retention Analysis:

Examine customer retention dynamics through an analysis of repeat orders. Calculate the frequency and contribution of repeat customers to overall sales. Deliverables:

Visualizations:

Deliver comprehensive visualizations such as histograms, kernel density plots, and heatmaps. Provide graphical representations of spending trends across genders, age groups, and regions. Insightful Reports:

Compile in-depth reports showcasing key findings from each analysis. Offer actionable insights and recommendations for marketing and operational strategies. Interactive Dashboards:

Develop interactive dashboards facilitating real-time data exploration. Enable stakeholders to engage interactively with the findings. Expected Impact:

Informed Decision-Making:

Empower decision-makers with a profound understanding of customer behavior. Tailored Marketing Strategies:

Establish the groundwork for tailored marketing strategies aligned with specific customer segments. Enhanced Customer Experience:

Provide insights contributing to an elevated customer experience, fostering satisfaction and loyalty. Technologies Used:

Python (Pandas, NumPy) Data Visualization: Matplotlib, Seaborn Jupyter Notebooks for analysis and documentation

About

Embark on a data-driven journey into e-commerce customer behavior using Python. Uncover captivating insights on spending trends, age group dynamics, and regional nuances. Elevate decision-making with compelling visualizations, detailed reports, and interactive dashboards. Harness the power of Python's Pandas, NumPy, and sophisticated data visualiza

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published