# Python & SQL Server Orders Analysis
This project demonstrates loading order data from a CSV file into SQL Server
and analysing it using SQL queries and Python.
The focus of the project is on validating order data and producing reliable
summary metrics using a combination of SQL and Python.
## Problem
Order data sourced from flat files (such as CSVs) often contains inconsistencies
that make analysis unreliable without additional validation.
Common issues include:
- duplicate order records
- missing or invalid date values
- discrepancies between stored and calculated totals
The aim of this project is to identify and address these issues so that order
and revenue summaries are based on trustworthy data.
## Approach
1. **Load data into SQL Server**
- Imported order data from a CSV file into a SQL Server table
- Ensured appropriate data types for analysis
2. **Validate and analyse using SQL**
- Ran summary queries to calculate order counts and totals
- Investigated potential data quality issues such as duplicates and null values
3. **Use Python for additional analysis**
- Connected to SQL Server using pyodbc
- Queried and analysed the data using Python
- Cross‑checked SQL results to ensure consistency
## Tech Stack
- Python
- SQL Server
- pyodbc
## Current Status
Initial data load and exploratory analysis complete.
Further validation checks and analysis can be added as the project evolves.