Diwali Sales Data Analysis

📊 Project Overview

In this project, I deeply analyzed the Diwali sales data using Python, NumPy, Pandas, Matplotlib, and Seaborn. The analysis includes insights based on various factors such as gender, age, amount spent, order count, city, and occupation. The goal was to identify significant patterns and trends, ultimately providing actionable insights to understand consumer behavior during Diwali better.

🔍 Data Analysis Steps

Data Exploration:

I started by exploring the data using Pandas, understanding the structure, checking the head, and identifying essential columns. Performed data cleaning and pre-processing steps to handle missing or incorrect values. Visualization:

Utilized Matplotlib and Seaborn to create various charts and diagrams, including Bar plots, pie charts, and histograms to understand distributions and trends. Heatmaps to visualize correlations between different variables. Analysis:

The analysis was segmented by key features:

Gender 🧑‍🦱👩‍🦱

Age Group 👶👨‍🦳

Amount Spent 💵

Order Count 🛒

City 🌆

Occupation 💼

📈 Key Findings

Gender-Based Insights:

Analyzed how sales varied between Male and Female consumers. Identified trends in purchasing patterns and product preferences.

Age Group Analysis:

The majority of purchases were made by consumers in the 26-35 years age group. T his group showed a higher tendency to purchase in categories like Electronics, Clothing, and Food.

City Insights:

Consumers from Uttar Pradesh, Maharashtra, and Karnataka showed the highest sales figures during Diwali.

Occupation-Based Insights:

Individuals working in the IT, Healthcare, and Aviation sectors made more purchases compared to other occupations.

Marriage and Gender Insights:

Married Women in the 26-35 years age group were the highest buyers, particularly from UP, Maharashtra, and Karnataka.

📍 Conclusion

From this analysis, we can conclude that married women, aged 26-35, living in UP, Maharashtra, and Karnataka, working in IT, Healthcare, and Aviation sectors, are the top buyers of products in Food, Clothing, and Electronics during Diwali.

Analysis The Data in a few Line

1. Performed data cleaning and manipulation
2. Performed exploratory Data Analysis(EDA) using matplotlib,pandas and seaborn libraries
3. improving customer experience by identifying potential customers across different States, Gender, Age Groups and Occupation