📊 Sales Trend Analysis Using SQL & Python

A real-world data project where I cleaned and transformed a sales dataset using Python, and performed time-based trend analysis using SQL revealing insights on monthly revenue and order volume.

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🧠 Objective

To explore trends in monthly sales and order volume using SQL-based aggregation, supported by Python-powered data cleaning.

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🚀 What I Did

✅ Cleaned the raw CSV using Python
✅ Transformed messy data into SQL-friendly format
✅ Wrote a SQL script to analyze sales and order trends
✅ Exported final cleaned dataset ready for dashboards or reports

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🛠️ Skillset Demonstrated

Category	Skills / Tools
👨‍💻 Programming	Python, Pandas
🗄️ Database Querying	SQL (MySQL, SQLite compatible)
🧹 Data Cleaning	Column renaming, date parsing, null handling
📊 Analysis	Time-based aggregation (revenue, orders)
📁 File Handling	CSV import/export

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📝 Dataset Overview

• Input File: Sample_sales.csv
• Output File: cleaned_sales_for_mysql.csv
• Important Fields: order_id, order_date, amount, product_id

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🧹 Data Cleaning Steps (Python)

import pandas as pd

# Load dataset
df = pd.read_csv("Sample_sales.csv", encoding="latin1")

# Clean column names
df.columns = df.columns.str.strip().str.lower().str.replace(' ', '_')

# Convert order_date to datetime
df['order_date'] = pd.to_datetime(df['order_date'], errors='coerce')

# Select and rename relevant columns
clean_df = df[['ordernumber', 'order_date', 'sales', 'productcode']].copy()
clean_df.columns = ['order_id', 'order_date', 'amount', 'product_id']

# Drop rows with missing values
clean_df.dropna(inplace=True)

# Save the cleaned data for SQL import
clean_df.to_csv("cleaned_sales_for_mysql.csv", index=False)