📊 Sales Analysis & Delivery Insights

📌 Overview

This project analyzes sales and delivery data to uncover patterns, trends, and actionable insights using Python.

The objective is to understand customer behavior, delivery preferences, and key factors influencing sales performance.

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🔧 Tools & Technologies

• Python
• Pandas (Data Cleaning & Analysis)
• NumPy (Numerical Computation)
• Matplotlib & Seaborn (Data Visualization)
• Plotly (Interactive Visualization)

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📂 Dataset

The dataset includes:

• Order details
• Sales information
• Product categories
• Delivery-related features

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📊 Analysis Performed

• Data cleaning and preprocessing
• Exploratory Data Analysis (EDA)
• Sales analysis by category and region
• Delivery performance evaluation
• Visualization of trends and patterns

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💡 Key Insights

• Most customers prefer Standard delivery, indicating cost is prioritized over speed.
• A few categories contribute significantly to total sales, highlighting key revenue drivers.
• Delivery time influences customer ordering behavior and patterns.

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📁 Project Structure

• sales_analysis_delivery_insights.ipynb → Main analysis notebook
• dataset.csv → Dataset used

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🚀 Conclusion

This analysis demonstrates how data can be transformed into meaningful insights to support better business decisions, especially in optimizing delivery strategies and identifying high-performing categories.

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🔗 Author

Ajay Kichara
Aspiring Data Analyst / Data Scientist

GitHub: https://github.com/ajaykichara