This project explores a real-world sales dataset to gain actionable insights into sales performance, customer behavior, and revenue trends. Through structured exploratory data analysis (EDA), the project identifies meaningful patterns, correlations, and opportunities that can support strategic decision-making.
Sales analytics is critical for businesses to understand revenue drivers, customer segments, and performance trends. This analysis dives into sales data to uncover:
- Patterns in product sales over time
- Relationships between sales and key business factors
- Insights that support forecasting and performance improvement
- Load and clean raw sales data
- Explore data using descriptive statistics
- Identify sales trends, seasonality, and customer behavior
- Visualize correlations and revenue drivers
- Generate insights through visual and statistical analysis
The dataset contains transactional sales records with attributes such as:
- Order dates and regions
- Product categories and quantities
- Sales amounts and profits
- Customer segments
This data enables meaningful exploration of business performance indicators.
- Language: Python
- Core Concepts: Exploratory Data Analysis, Revenue Trends, Correlation Analysis
- Libraries & Tools:
pandas,numpy,matplotlib,seaborn - Platform: Jupyter Notebook
- Loaded and inspected the sales dataset to understand structure and missing values
- Performed data cleaning and preprocessing
- Conducted descriptive statistics to summarize key features
- Analyzed sales and profit trends across time and categories
- Examined relationships using correlation analysis
- Created visualizations to highlight patterns and insights
- Identified seasonal sales trends, with peak performance during certain periods
- Observed strong correlations between order value and customer segments
- Recognized top-performing product categories and regions
- Generated actionable insights for revenue optimization and sales strategy