Sales Analysis and Forecasting

After 2+ yrs break from coding I resumed crunching data again. Hope I can continue..

Project Overview

This project analyzes sales data to identify trends and forecast future sales using Python. The dataset contains yearly sales data, including profit centers, product groups, general ledger names, regions, and countries. Various visualizations and predictive analysis techniques are applied to gain insights and make data-driven decisions.

📊 Data Analysis and Visualizations

1️⃣ Year-wise Sales Graph

• Visualizes total sales for each fiscal year.
• Helps identify trends in revenue growth or decline over time.

2️⃣ Yearly Quantity-wise Sales

• Displays the total quantity of products sold per year.
• Useful for understanding demand patterns.

3️⃣ Region-wise Sales

• Compares sales performance across different regions.
• Helps identify high-performing and low-performing regions.

4️⃣ Country-wise Sales

• Breaks down sales figures by country.
• Useful for evaluating market penetration and country-wise revenue contribution.

5️⃣ Sales Trends

• Identifies patterns in sales over multiple years.
• Helps in making strategic business decisions.

6️⃣ Sales Heatmap

• A heatmap visualization to show correlations between different sales parameters.
• Helps in finding key drivers of sales performance.

7️⃣ Sales Forecast

• Uses ARIMA time series forecasting to predict future sales.
• Provides a data-driven approach for sales planning.

Tec Stack

• Python (Pandas, NumPy, Matplotlib, Seaborn, Statsmodels, Scikit-learn)
• Jupyter Notebook / Google Colab for analysis
• Git & GitHub for version control

To Do

• Implement deep learning models (LSTMs) for forecasting.
• Add interactive dashboards using Streamlit or Dash.
• Automate data updates using APIs.

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