🛒 Amazon Sale Report Data Cleaning & Exploratory Analysis

This project performs data cleaning, KPI computation, and exploratory analysis on a real-world Amazon Retail Sales dataset.
It also includes an Object-Oriented Python class (SalesAnalyzer) encapsulating all analytics logic.

👉 Live Streamlit App: https://assignmentpythonweek1.streamlit.app/

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📂 Project Structure

📁 Project/
├── Amazon Sale Report.csv
├── Cleaned_Amazon_Sale_Report.csv
├── EDA.ipynb
├── Kpi Analysis.ipynb
├── SalesAnalyzer.py
├── amazon.ipynb
├── streamlit_app.py (Streamlit Dashboard)
├── outlier_boxplot.png
│──monthly_revenue_trend.png
│── category_sales.png
│── top_states_sales.png
│── order_status_distribution.png
│── b2b_vs_b2c.png
│
└── README.md

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🎯 Project Goals

• Clean raw retail sales data (missing values, duplicates, formatting issues)
• Perform type conversion (dates, numeric fields, booleans, categories)
• Compute key business KPIs:
  • Monthly Revenue
  • Region-wise Sales
  • Profit Margin (%)
  • Average Order Value (AOV)
  • Cancellation Rate
  • Category-wise Sales
  • B2B vs B2C Sales Split
• Visualize insights using Matplotlib and Plotly
• Implement an OOP-based SalesAnalyzer class to automate analysis
• Interactive Streamlit Dashboard for real-time exploration

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🧹 1. Data Cleaning Summary

Key cleaning operations performed:

✔ Column Normalization

• Trimmed column name spaces
• Dropped index and Unnamed:22

✔ Datatype Fixes

• Date → datetime
• Amount & Qty → numeric
• ship-postal-code → string
• B2B → boolean

✔ Text Standardization

• Title case for cities/states
• Uppercase for sizes
• Cleaned category names

✔ Intelligent Missing Value Handling

• Courier Status → "Pending"
• Promotion IDs → "No Promotion"
• Currency → "INR"
• Fulfilled-by assigned using Fulfilment logic

✔ Special Logic

Cancelled orders are assigned Amount = 0.

✔ Final Output

Cleaned dataset saved as: Cleaned_Amazon_Sale_Report.csv

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📊 2. KPI Analysis

All KPI logic is implemented in SalesAnalyzer.py using OOP.

📈 Monthly Revenue Trend

🗺 Region-wise Sales (Top States)

🏷 Category-wise Sales Distribution

👥 B2B vs B2C Comparison

📦 Order Status Distribution

⚠️ Outlier Detection (Sale Amount)

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🧮 3. Additional KPIs Computed

• Profit Margin %: Assuming baseline cost = 65% of selling price
• Average Order Value (AOV): Total Revenue / Number of Orders
• Cancellation Rate: Percentage of orders marked Cancelled
• Top Performing Categories: By revenue and quantity
• Fulfilment Performance: Amazon vs Merchant comparison

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🧠 4. SalesAnalyzer OOP Class

The project includes a full OOP implementation in SalesAnalyzer.py

Features

• Monthly Revenue Analysis
• Region Sales Breakdown
• Average Order Value Calculation
• Profit Margin Computation
• Cancellation Rate Analysis
• Automated Trend Charts
• Outlier Detection Visualizations

Example Usage

from SalesAnalyzer import SalesAnalyzer
import pandas as pd

df = pd.read_csv("Cleaned_Amazon_Sale_Report.csv")
analyzer = SalesAnalyzer(df)

# Generate comprehensive report
analyzer.generate_report()

# Plot visualizations
analyzer.plot_monthly_revenue()
analyzer.plot_region_sales()
analyzer.plot_categories()
analyzer.plot_outliers()

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Here is the completed section for your README, polished and ready to copy–paste, including your Streamlit app link and full details:

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📊 5. Interactive Streamlit Dashboard

This project includes a fully interactive Streamlit Dashboard where you can explore Amazon Retail Sales visually.

🚀 Launch Dashboard Locally

To run the dashboard on your machine:

streamlit run streamlit_app.py

Make sure the required packages are installed:

pip install streamlit pandas numpy matplotlib seaborn plotly pyarrow

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🌐 Live Hosted Dashboard

You can view the hosted version of the dashboard here:

👉 Live Streamlit App: https://assignmentpythonweek1.streamlit.app/

This dashboard includes:

• 📈 Monthly Revenue Trends
• 🛒 Category-wise Sales
• 🗺 Top States by Sales
• 👥 B2B vs B2C Split
• 📦 Order Status Distribution
• ⚠️ Outlier Detection
• 🧹 Clean Data Preview
• 🔍 Filters for Date, Category, Fulfilment, etc.

All visualizations are interactive and allow you to explore the dataset in depth.

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Dashboard Features

• Overview Tab: Key metrics and monthly trends
• Regional Analysis: State-wise performance breakdown
• Product Insights: Category and SKU analysis
• Deep Dive: Custom filters and detailed exploration
• AI Insights: Automated recommendations and anomaly detection

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📓 6. Jupyter Notebooks

✔ EDA.ipynb

Data cleaning + descriptive statistics

✔ Kpi Analysis.ipynb

KPI computation + visualizations

✔ amazon.ipynb

Combined workflow for cleaning + analysis

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🔧 7. Installation & Setup

Clone the repository

git clone https://github.com/satya-blend360/Assignment_Python_Week1.git

Install dependencies

pip install pandas numpy matplotlib seaborn plotly streamlit

Run Jupyter Notebook

jupyter notebook

Open any of the notebooks:

• EDA.ipynb
• Kpi Analysis.ipynb
• amazon.ipynb

Or run Python script

python SalesAnalyzer.py

Launch Streamlit Dashboard

streamlit run app.py

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📦 8. Dataset Information

• Total Records: 128,971 orders
• Date Range: April 2022 - March 2023
• Columns: 26 features including order details, product info, shipping data, and financial metrics
• Categories: Kurta, Set, Western Dress, Top, and more
• Geographic Coverage: Multiple states across India

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🏆 9. Key Findings

• Top performing state contributes 25%+ of total revenue
• B2B orders have 40% higher average order value than B2C
• Peak sales months align with festive seasons
• Cancellation rate averages around 12-15%
• Amazon fulfillment shows better delivery success rates

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🚀 10. Technologies Used

• Python 3.12+
• Pandas: Data manipulation and cleaning
• NumPy: Numerical computations
• Matplotlib: Static visualizations
• Seaborn: Statistical plots
• Plotly: Interactive charts
• Streamlit: Web dashboard
• Jupyter: Interactive notebooks

🏁 Conclusion

This project demonstrates:

✅ Complete retail data cleaning workflow
✅ KPI-driven business analytics
✅ Rich visualizations for decision-making
✅ Clean and scalable Object-Oriented Python design
✅ Interactive dashboard for real-time insights
✅ Professional documentation & reproducible results

Perfect for showcasing Data Engineering, Analytics, and Python Development skills!