You will get a time series forecasting model for your business data
Project details
Need to predict future sales, demand, or any time-based metric? I build time series forecasting models using statistical and machine learning approaches, comparing multiple methods to find what fits your data best. I handle seasonality detection, feature engineering, and deliver a clean evaluated model with forecast visualizations. Works for sales forecasting, demand planning, price prediction, and any sequential business data.
Machine Learning Tools
NumPy, pandas, Python Scikit-Learn, scikit-learn, XGBoostWhat's included
| Service Tiers |
Starter
$25
|
Standard
$50
|
Advanced
$85
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 8 days |
Number of Revisions | 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
+$8 - $25
Additional Revision
+$5
Additional Graph/Chart
(+ 1 Day)
+$5
Model Documentation
(+ 1 Day)
+$8
Source Code
(+ 2 Days)
+$20
Multiple Forecast Horizons
(+ 1 Day)
+$15
Interactive Forecast Dashboard
(+ 2 Days)
+$20Frequently asked questions
About Abdullah
Junior AI/ML Engineer & Data Science Student
Cairo, Egypt - 3:31 pm local time
With hands-on experience in machine learning and data science, I specialize in:
• Building and deploying ML models (classification, regression, NLP)
• Data analysis & visualization (Python, Pandas, Matplotlib)
• Deep Learning (TensorFlow, PyTorch)
• Data cleaning, preprocessing & feature engineering
I focus on delivering practical AI solutions that actually work in the real world — not just in theory.
Feel free to message me and let's discuss how I can help with your project!
Steps for completing your project
After purchasing the project, send requirements so Abdullah can start the project.
Delivery time starts when Abdullah receives requirements from you.
Abdullah works on your project following the steps below.
Revisions may occur after the delivery date.
Data Analysis
Explore the time series data, check for seasonality, trends, and missing periods
Model Training & Comparison
Train and compare forecasting models to select the best fit for your data pattern