DIWALI_SALES_ANALYSIS

Analyse Diwali Sales using Python Libraries.

OBJECTIVES

• Analyse Diwali sales on the basis of different products, gender, state, age, occupation, and zone areas of the customers.
• Improving customer experience by analyzing sales data.
• Increase revenue.

SALES ANALYSIS

• Loading .csv file using pandas.
• Performed Data Cleaning and Data Manipulation.
• Performed Exploratory Data Analysis (EDA) using Pandas, NumPy, Matplotlib, and Seaborn Libraries.
• Improved Customer experience by identifying potential customers across different states, occupations, gender, and age groups.
• Improved sales by identifying the most selling product categories and products, which can help to plan inventory and hence meet the demands.

PREVIEW

SALES INSIGHT

1. Females are the majority of buyers.
2. Women have a higher purchasing power compared to men.
3. Most orders and total sales/amount are from Uttar Pradesh, Maharashtra, and Karnataka.
4. Married women tend to purchase items from the Food, Clothing, and Electronics categories frequently.
5. The Food, Clothing, and Electronics categories are the most sold products.

CONCLUSION

Women who are married and aged between 26 to 35 residing in Uttar Pradesh, Maharashtra, and Karnataka, and working in the IT, Healthcare, and Aviation industries, tend to purchase items from the Food, Clothing, and Electronics categories more frequently.