Sales Data Analytics

Analyzing sales trends, customer behavior, and revenue insights using Data Analytics.

🔹 Overview

This project provides in-depth sales analytics, identifying trends, customer segments, and revenue drivers. It helps businesses make data-driven decisions for sales growth.

✅ Key Insights:

• Monthly and yearly sales trends 📈
• Top-performing products & customer segments 🛒
• Revenue growth analysis 💰
• Sales forecasting using Time Series models 📊

🔹 Tech Stack

• 🟢 Python (Pandas, NumPy, Matplotlib, Seaborn)
• 🟢 SQL (for data extraction & transformation)
• 🟢 Power BI / Tableau (for visualization)
• 🟢 Scikit-learn (for forecasting & predictive analysis)

🔹 Dataset

• Sales transactions dataset (CSV format)
• Data includes order date, product details, customer ID, revenue, and location

🔹 Installation & Setup

# Clone the repository
git clone https://github.com/Rishita-rm/Sales_DataAnalytics.git

# Navigate to the project folder
cd Sales_DataAnalytics

# Install dependencies
pip install -r requirements.txt

# Run the analysis script
python sales_analysis.py

🔹 Implementation Steps

1️⃣ Data Cleaning & Preprocessing
2️⃣ Exploratory Data Analysis (EDA)
3️⃣ Sales Trends Analysis (Yearly, Monthly, Weekly)
4️⃣ Customer Segmentation (RFM Analysis)
5️⃣ Sales Forecasting (Time Series Models)
6️⃣ Data Visualization with Power BI / Tableau

🔹 Results & Visualizations

📊 Sales Trends Analysis:

• Sales increased by 15% YoY 🚀
• Peak sales observed in December (Holiday Season Effect) 🎄

📌 Top Products:

• Product A: Highest revenue generator 💸
• Product B: Most frequently purchased 🛍️

🔹 How to Use?

1. Load the dataset (sales_data.csv)
2. Run sales_analysis.py to analyze trends
3. View insights using Power BI / Tableau dashboards
4. Use forecasting models to predict future sales

🔹 Future Improvements

✅ Automate data extraction from live databases
✅ Integrate Machine Learning for demand forecasting
✅ Develop a real-time analytics dashboard

🔹 Contributing

Want to contribute? Follow these steps:

1. Fork the repository
2. Create a new branch (feature-xyz)
3. Commit changes
4. Push to the branch
5. Open a Pull Request