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$
USD
I will forecast sales and demand using python time series
Data Scientist, Data Analyst , Data Analytics
Hi, I'm Muneeb Ullah, a Python Data Analyst focused on turning raw data into clear, useful insights. I specialize in Python, Pandas, NumPy, Matplotlib, Seaborn, and Excel. I provide data cleaning, pre...
About this Gig
Are you struggling to predict future sales, manage inventory, or understand business demand trends? I will help you turn your historical data into accurate forecasts using Python and Machine Learning.
Whether you run an e-commerce store, retail business, or managing inventory, I build reliable time-series models that give you clear visibility into future performance and prevent stockouts or overstocking.
What I Can Do for You:
- Time Series Forecasting (Sales, Revenue, Demand, Traffic)
- Trend, Seasonality, & Pattern Analysis
- Advanced Data Cleaning & Preprocessing (handling missing dates & outliers)
- Predictive Models (ARIMA, SARIMA, Prophet, XGBoost, Random Forest)
- Interactive & High-Resolution Charts (Matplotlib, Seaborn, Plotly)
- Performance Metrics (MAE, RMSE, MAPE) & Business Insights Report
Why Choose Me?
- Tailored Solutions: Customized models built specifically for your business goals.
- Clean Code: Fully documented Python source code provided.
- Actionable Insights: Easy-to-understand charts and visual reportsno technical jargon.
Ready to make data-driven decisions? Order now or message me with your dataset to discuss your project!
Programming language:
Python
•
SQL
•
Colab
Frameworks:
Scikit-learn
•
Panda
Tools:
Jupyter Notebook
•
OpenCV
•
Excel
•
Colab
My Portfolio
FAQ
What file formats or datasets do you accept?
I accept Excel files (.xlsx, .xls), CSV, TSV, and Google Sheets. If your data is in a database or another format, please contact me before ordering.
How much historical data do I need to provide for accurate forecasting?
Ideally, at least 12 to 24 months of historical data (daily, weekly, or monthly) works best to detect seasonality and long-term trends. However, I can also work with shorter timeframes depending on your requirements.
What Python models or techniques do you use for forecasting?
I use a mix of statistical and machine learning models depending on your data complexity, including ARIMA, SARIMA, Prophet, Exponential Smoothing, Random Forest, and XGBoost.
Will I receive the source code with the project?
Yes! Full Python source code (Jupyter Notebook .ipynb or .py script) with clean documentation is included in the Standard and Premium packages.
What if my dataset has missing values, gaps, or messy dates?
Don't worry. Data cleaning and preprocessing (handling missing dates, outliers, and formatting) are included in all packages to ensure the dataset is analysis-ready.
