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I will build a local ml ecommerce forecasting app in streamlit sql
Data Scinece and AI Consultant
Hi, I’m SOHEL RANA, a dedicated Data Driven AI Services focused on helping businesses grow.
With 4+ years of hands-on experience, I specialize in delivering high-quality, impactful results in Data Sci...
About this Gig
Stop paying recurring monthly SaaS cloud fees. Keep your enterprise data 100% private and GDPR-compliant with a custom, local-first Python AI solution.
I build standalone, offline Machine Learning (ML) and Deep Learning (DL) forecasting web apps tailored for Amazon, Shopify, and retail brands.
Delivery Workflow:
You receive an offline workspace. Unzip it, open your terminal, type python run.py, and the dashboard auto-launches natively in your local browser window.
Core Technical Features:
- Dual Integration: Connect via CSV/Excel spreadsheets OR pull directly from live local databases (MySQL, PostgreSQL, SQLite).
- Predictive AI: High-precision time-series forecasting (XGBoost, Prophet, or LSTM) to map seasonal demand and restock points.
- Interactive UI: Dynamic charts, restock matrices, and download summaries.
Tiers at a Glance:
- Basic: Data ledger logic audit & preprocessing Python cleanup script.
- Standard: Core ML engine + Streamlit uploader app for Excel/CSV files.
- Premium: Advanced predictive ML engine + Direct SQL live database integration.
Please message me to check your dataset schema before ordering!
Programming language:
Python
•
SQL
•
Colab
Frameworks:
Scikit-learn
•
Panda
Tools:
Jupyter Notebook
•
TensorFlow
•
Excel
•
Colab
Other Data Science & ML Services I Offer
FAQ
How does the app connect to my database?
I configure secure Python SQL connectors (like SQLAlchemy or psycopg2) to stream your transaction tables safely into the Streamlit UI.
Will it slow down my SQL server?
No. I implement Streamlit @st.cache_data functions to cache query results, preventing constant server hits and keeping the app lightning-fast.
What database engines are supported?
Any database with a Python driver, including PostgreSQL, MySQL, SQLite, Snowflake, and BigQuery.
How are data gaps or missing values handled?
The automated pipeline includes SQL/Pandas data-cleaning scripts that forward-fill or zero-fill missing transaction days automatically.
What forecasting algorithms do you implement?
I primarily use Prophet for seasonality-heavy retail tracking, alongside scikit-learn models for baseline linear regressions.
Can the app handle holidays and Black Friday spikes?
Yes. I inject custom historical holiday events into the model via SQL to ensure accurate tracking of annual e-commerce spikes.
Can I export the data from the app?
Yes. Users can download any generated forecast table instantly as a CSV or Excel file using built-in Streamlit buttons.
Is "What-If" scenario testing included?
Yes. I build interactive sidebar sliders allowing you to simulate revenue changes based on adjusting prices or ad spend.
