You will get a custom sales forecasting model built in Python
Project details
I will build a professional sales forecasting solution using Python and machine learning. Your project may include data cleaning, leakage-safe feature engineering, baseline comparison, time-based validation, model evaluation, forecast charts, and export-ready results. I focus on clear, business-friendly outputs that can support sales planning, inventory decisions, staffing, and replenishment.
Machine Learning Tools
Microsoft Excel, Microsoft Power BI, NumPy, pandas, Python, Python Scikit-Learn, scikit-learn, TensorFlow, XGBoostWhat's included
| Service Tiers |
Starter
$45
|
Standard
$95
|
Advanced
$180
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 days |
Number of Revisions | 1 | 2 | 2 |
Number of Model Variations | 1 | 3 | 5 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 2 | 5 | 8 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | - | - | |
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$25 - $65
Additional Model Variation
(+ 2 Days)
+$20
Additional Scenario
(+ 1 Day)
+$15
Additional Graph/Chart
(+ 1 Day)
+$5
Model Documentation
(+ 1 Day)
+$20Frequently asked questions
About Mustafa Efe
Python Data Analyst | Sales & Demand Forecasting | Predictive Modeling
Ankara, Turkey - 12:32 am local time
My background combines Industrial Engineering, data analysis, machine learning, and real-world forecasting. In my award-winning capstone project, I worked with hourly electricity demand data from 2018 to 2025 and developed a complete short-term load forecasting workflow using historical consumption, weather, calendar, and holiday variables.
The project compared five standalone machine learning and deep learning models, including XGBoost, LightGBM, MLP, LSTM, and Oblique Random Forest. The best-performing model achieved a test MAPE of 2.456%. The work also included feature engineering, model validation, forecast error analysis, sensitivity analysis, and a decision-support interface for reviewing model results.
I can help you with:
• Sales and demand forecasting
• Time-series analysis and data preparation
• Regression and predictive modeling
• Feature engineering and feature selection
• Model comparison, validation, and tuning
• Forecast accuracy and residual analysis
• Actual-versus-forecast visualizations
• Documented Jupyter Notebooks
• Forecast outputs in CSV or Excel format
• Clear summaries that connect model results to planning decisions
My core tools include Python, Pandas, NumPy, Scikit-learn, XGBoost, LightGBM, TensorFlow/Keras, Matplotlib, Jupyter Notebook, and Excel.
I select methods based on the structure of the data, forecast horizon, business objective, and required level of interpretability—not simply on model complexity. My goal is to deliver a solution that is technically reliable, clearly documented, and useful for real business decisions.
Please feel free to contact me with a brief description of your data, forecasting objective, and expected deliverables.
Steps for completing your project
After purchasing the project, send requirements so Mustafa Efe can start the project.
Delivery time starts when Mustafa Efe receives requirements from you.
Mustafa Efe works on your project following the steps below.
Revisions may occur after the delivery date.
Data review and project setup
I review the dataset, target variable, forecast horizon, business context, and requested outputs.
Data cleaning and preparation
I clean the data, validate date fields, handle missing values, and prepare the dataset for forecasting.