You will get a forecast audit and model improvement plan
Rising Talent
Project details
If your forecast is underperforming, unstable across certain periods, or you are unsure whether the model can be improved, I will audit the full forecasting workflow and identify where performance is being lost.
I will review the data, validation design, baseline performance, error patterns, seasonality, feature setup and modelling approach. Depending on the package selected, I will also test targeted improvements and compare them consistently against the existing model or an appropriate baseline.
You will receive clear diagnostic findings, charts, practical recommendations and an improvement roadmap. Standard and Advanced packages also include tested model improvements and code handover where applicable.
This service works well for demand, sales, inventory, energy, price and other time-series forecasting problems.
Please note that model improvement depends on the available data and problem structure, so I do not promise an artificial percentage improvement before the audit.
I will review the data, validation design, baseline performance, error patterns, seasonality, feature setup and modelling approach. Depending on the package selected, I will also test targeted improvements and compare them consistently against the existing model or an appropriate baseline.
You will receive clear diagnostic findings, charts, practical recommendations and an improvement roadmap. Standard and Advanced packages also include tested model improvements and code handover where applicable.
This service works well for demand, sales, inventory, energy, price and other time-series forecasting problems.
Please note that model improvement depends on the available data and problem structure, so I do not promise an artificial percentage improvement before the audit.
Machine Learning Tools
Azure Machine Learning, Microsoft Power BI, NumPy, pandas, Python, scikit-learn, SciPy, SQL, XGBoostWhat's included
| Service Tiers |
Starter
$650
|
Standard
$1,050
|
Advanced
$1,500
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 days |
Number of Revisions | 1 | 1 | 2 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 3 | 5 | 8 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | - | - | - |
Source Code | - |
Optional add-ons
You can add these on the next page.
Additional Model Variation
(+ 1 Day)
+$200
Additional Scenario
(+ 1 Day)
+$125Frequently asked questions
About Varun
Forecasting & Data Analytics Specialist | Python, SQL, Power BI
Birmingham, United Kingdom - 4:08 pm local time
My experience spans energy-market forecasting, retail demand forecasting, commercial analytics and enterprise data engineering. At E.ON Energy Markets, I improved hourly forecast RMSE by 0.3 to 0.5 GWh/h, diagnosed holiday-period model deterioration and reduced holiday RMSE by about 18%. In retail, I built multi-horizon demand forecasts across roughly 7,000 SKUs, with recorded accuracy improvements of 28% weekly, 19% monthly and 37% quarterly.
I can help with:
• Demand, sales and time-series forecasting
• Forecast audits, error analysis and model improvement
• Python and SQL reporting automation
• Data cleaning, transformation and ETL workflows
• Power BI analytics and decision dashboards
• Predictive modelling, segmentation and scenario analysis
My core stack includes Python, SQL, scikit-learn, XGBoost, Power BI, Snowflake, Azure Data Factory and Azure Databricks.
I focus on clear scope, reproducible analysis and outputs that are actually useful for business decisions.
If you already have a dataset, forecast or recurring reporting workflow that needs improving, send me a brief description of the problem and I can quickly assess the best way to approach it.
Steps for completing your project
After purchasing the project, send requirements so Varun can start the project.
Delivery time starts when Varun receives requirements from you.
Varun works on your project following the steps below.
Revisions may occur after the delivery date.
Review the data and forecasting setup
I will validate the dataset, target, forecast horizon, current methodology and evaluation setup, and flag any structural data or validation issues.
Benchmark forecast performance
I will compare current performance against appropriate baselines and analyse error patterns by time period, segment, seasonality and forecast horizon.