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Python Retail Sales Trend Analysis
예산
$12,500~$37,500 INR
예상 기간
2~3주
난이도
보통
기술 스택
Python
Pandas
NumPy
Data Processing
Data Analysis
Data Mining
Data Visualization
Matplotlib
Seaborn
Jupyter Notebook
SciPy
Software Architecture
AI 분석 요약
이 프로젝트는 소매 판매 CSV 데이터를 Python과 관련 라이브러리(Pandas, NumPy)를 활용하여 분석하고 시각화하는 작업입니다. 제품별 매출 동향 및 통계적으로 유의미한 상승/하락 추세를 식별하여 주피터 노트북 형태로 제공해야 하며, 데이터 처리, 통계 분석, 시각화 역량이 필수적입니다.
프로젝트 원문 설명
I have a raw CSV export of customer-level transactions from our retail platform and I need clear, data-driven insight into which products are really driving revenue. The goal is simple: surface product-level sales trends so I can see which SKUs consistently outperform and which ones lag behind, all viewed through the lens of revenue generated rather than just unit counts.
You’re free to structure the workflow as you see fit, but I expect the core analysis to happen in Python, leveraging pandas and NumPy for data wrangling and aggregation. I’ll supply the dataset along with a brief data dictionary; you return a well-commented Jupyter notebook (or .py script) that:
• Cleans and normalises the raw data
• Calculates revenue by product over selectable time windows
• Highlights statistically significant up- or down-trends
• Presents the findings in clear tables and a few concise Matplotlib or Seaborn visuals
Deliverables are complete when the notebook runs end-to-end on my machine, reproduces the same figures, and includes a short executive summary (markdown cell is fine) I can lift straight into a slide deck.
You’re free to structure the workflow as you see fit, but I expect the core analysis to happen in Python, leveraging pandas and NumPy for data wrangling and aggregation. I’ll supply the dataset along with a brief data dictionary; you return a well-commented Jupyter notebook (or .py script) that:
• Cleans and normalises the raw data
• Calculates revenue by product over selectable time windows
• Highlights statistically significant up- or down-trends
• Presents the findings in clear tables and a few concise Matplotlib or Seaborn visuals
Deliverables are complete when the notebook runs end-to-end on my machine, reproduces the same figures, and includes a short executive summary (markdown cell is fine) I can lift straight into a slide deck.
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