Sales Forecasting via ML Analytics

@bimzaman000

Hey! I’ve gone through your requirements and your project fits perfectly with my expertise. I have an MPhil in Statistics and strong experience in data analysis, data cleaning, and ML-based forecasting using models like Prophet, LSTM, and XGBoost. I can handle your sales data end-to-end—cleaning, feature engineering (seasonality, promotions, trends), and building an accurate forecasting model. I also have solid experience in Power BI, so I can create clear, interactive dashboards with MAE/MAPE metrics and insights. I will deliver clean data, reproducible code, forecasts for 3/6/12 months, and a concise summary of key findings and recommendations. I ensure quality work, accuracy, and on-time delivery. Samples are available on request. Looking forward to your response. Thank you!

@jhonyo669

Hello. With a Master’s degree in Statistics, a mathematical background, and deep specialization in regime‑switching time series models, I am uniquely positioned to handle the complexities in your sales data such as sudden changes due to promotions, stockouts, or seasonal shifts. Unlike standard ML models that assume a single stable relationship, my approach detects and adapts to different regimes (e.g., normal days vs. promotion days vs. post‑stockout recovery). This reduces forecast error and makes your projection roadmap robust to real‑world disruptions. Let’s chat to review a data sample and finalize milestones. I am confident that combining my mathematical statistics background with regime‑switching expertise will deliver a forecast that is both accurate and actionable.

@EightVeer

Hi there, We can help turn the historical sales records into a reproducible forecasting workflow with clean data preparation, feature engineering, model benchmarking, and an ML-based forecast that can be retrained as new data arrives. We will also provide clear visuals, error metrics, and concise commentary on the main demand drivers so the results are easy to review and hand over. To align the work well, we would first review a sample of the data and confirm the forecast granularity, the hold-out approach, and any periods that should be treated carefully such as stockouts or exceptional campaigns. We will keep all communication and payment through Freelancer. Best Regards, 8veer

@saba106

I am an expert statistician, Research Writer, and data analyst with more than eight years of experience. I have full command of Excel analysis, SPSS, STATA, R LANGUAGE, AND PYTHON. I am an expert in creating time series prediction models, working with survey data, conducting marketing analysis, building estimators, and medical analysis. I am a perfect match for your project share other details of the work so I can start working on your project. Will complete task on time.

@tangramua

Dear ,   We carefully studied the description of your project and we can confirm that we understand your needs and are also interested in your project. Our team has the necessary resources to start your project as soon as possible and complete it in a very short time.   We are 25 years in this business and our technical specialists have strong experience in Statistics, R Programming Language, Statistical Analysis, SPSS Statistics, Data Visualization, Data Analysis, Regression Analysis, Time Series Analysis and other technologies relevant to your project.   Please, review our profile https://www.freelancer.com/u/tangramua where you can find detailed information about our company, our portfolio, and the client's recent reviews.   Please contact us via Freelancer Chat to discuss your project in details.    Best regards, Sales department Tangram Canada Inc.     

@devendrathakur12

i’ve done very similar recently, building sales forecasts using Prophet + XGBoost with feature engineering on POS + ecommerce data. Do you have consistent product/category IDs across all sources or will mapping be required? How are promotions recorded, as flags, discounts, or separate tables? I suggest combining Prophet (seasonality) with XGBoost (features) to improve accuracy beyond single models. I also suggest adding lag features and rolling averages because they stabilize predictions and reduce noise. I will clean and unify datasets, engineer features, and build baseline + ML models with cross-validation. Then I will tune for <10% MAPE, generate forecasts, and build a Power BI dashboard with metrics and insights. Best, Dev S.

@Shafqat2567

I’m a data professional with strong expertise in machine learning–based forecasting, particularly LSTM networks, and I’d be glad to support your project. I have hands-on experience working with retail and transactional datasets, including POS and e-commerce data, and can share relevant LSTM-based forecasting work upon request. I will begin by thoroughly exploring and cleaning your historical sales data—handling missing values, outliers, and inconsistencies. Then, I’ll engineer meaningful features such as seasonality patterns, promotional effects, and regional variations to ensure the model captures real business dynamics. For forecasting, I will implement and compare multiple approaches, including LSTM, gradient boosting, and Prophet, while using classical models as benchmarks. The final solution will focus on a robust ML model that is accurate, scalable, and easy to retrain with new data. You will receive clear forecasts for 3, 6, and 12 months, along with performance metrics like MAE and MAPE (targeting <10%). I will also develop an interactive dashboard in Power BI or Tableau (or a well-structured Jupyter notebook) to visualize trends, seasonality, and key drivers. Deliverables will include clean data, fully documented code, forecast outputs, and a concise presentation with actionable insights and next steps. Happy to review sample data and discuss timelines.

@SolutionMart

Sales forecasting accuracy hinges on proper feature engineering and model selection—many teams fail by overlooking seasonality decomposition and promotional impact quantification before modeling. This project sits directly within my wheelhouse. The workflow spans data cleaning and exploratory analysis, statistical testing for stationarity, feature engineering for temporal patterns, and ML model development using gradient boosted trees, LSTM, or Prophet. I've executed similar projects using R, Python, SPSS, and E-Views for time-series decomposition and regression diagnostics. Deliverables will include a fully documented, reproducible pipeline with cleaned dataset, commented code, and three-horizon forecasts (3/6/12 months) validated against a hold-out set. The MAPE threshold of under 10% is achievable with proper cross-validation and hyperparameter tuning. Dashboard visualization will embed error metrics and business driver commentary—either Jupyter notebook or Power BI depending on your infrastructure. Hand-off includes retraining protocols and assumptions documentation. Timeline and data samples needed upfront to establish baseline performance and confirm data quality thresholds.

@zarminagull189

Hello, I understand you need accurate ML-driven sales forecasts from POS and e‑commerce exports, with clean data, reproducible code, and visual dashboards. Proposed solution: - Data exploration, cleaning (missing values, outliers), and feature engineering for seasonality, promos, channels - Model experiments: Gradient-boosted trees (LightGBM/XGBoost), Prophet and LSTM as needed; classical models as benchmarks - Hyperparameter tuning, cross-validation, and hold-out testing to target <10% MAPE - Dashboard (Power BI/Tableau) or notebook visuals, plus retraining instructions Deliverables: - Cleaned documented dataset - Fully commented notebook + code for retraining - Forecast CSVs (3/6/12 months) and visuals - Slide summary with assumptions and next steps Budget & timeline: 15 EUR; delivery in 10 days. Portfolio: https://www.freelancer.com/u/zarminagull189 Best regards,

@Lazarone1

Hi, that’s great to hear! Your project closely aligns with one I recently worked. In that project, I built a full ML-driven forecasting pipeline using gradient-boosted models, LSTM networks, and Prophet, with automated feature engineering for seasonality, promotions, and regional trends, as well as a clean visual reporting layer. Your goal of transforming historical sales data into accurate, ML-powered projections matches the workflow I handled, from data cleaning and anomaly detection to building retrainable forecasting models and producing dashboards with MAE and MAPE tracking. I can apply the same structured approach to your POS and e‑commerce datasets, ensuring robust preprocessing, engineered features for campaigns and product categories, and clear visuals for your 3-, 6-, and 12‑month forecasts. The deliverables you listed align very well with my usual handover process, including reproducible notebooks and documented datasets. I’d be glad to connect and share my experience in more detail over chat. Thank you. Best regards, Lazar

@ayusha2808

As a seasoned data scientist with profound skills in statistical analysis and modeling, your search for an expert to transform your sales records into accurate forecasts has brought you to the right place. I understand that your primary goal is deriving reliable estimates and projections for your business using an ML approach, and I have rich experience in creating ML models leveraging on platforms like gradient-boosted trees, LSTM, and Prophet. I also possess a solid understanding of classical time-series and regression techniques – a distinct advantage when it comes down to selecting the best model for your specific sales data. Over my 8+ years of experience, I have developed a strong acumen in handling large enterprise datasets for organizations and delivering insights that drive tangible outcomes. I am adept at transforming complex raw data into actionable insights through clean code that is both reproducible and user-friendly. My records have made it evident that I can guarantee a forecast error under 10% MAPE on a hold-out set – one of your acceptance criteria.

@MohammadNazirctn

Interested! I am experience in machine learning and data science like project in pso , stock exchange data for sale forecast also have complete many national and international project in same domain. I will provide complete details of each step also training to perform tasks if you needed. Complete details of each code steps one by one in Jupiter notebook also provide complete details document and complete final result according to your give instruction. If you are interested then let me know. I am available to assist you.

@quan9824

Hi, Your project is exactly in my wheelhouse. I’ve built end-to-end forecasting pipelines on messy POS + e-commerce data, turning raw transactions into reliable, retrainable ML models and business-ready dashboards. Approach (focused & production-ready): Data prep: Clean/merge sources, handle missing values, outliers, and inconsistencies Feature engineering: Seasonality (daily/weekly/annual), promotions, holidays, regional trends, lag features Modeling: Benchmark (ARIMA/ETS) → ML core (XGBoost/LightGBM, Prophet, or LSTM if needed) Validation: Time-based split, target ≤10% MAPE with MAE/MAPE reporting Explainability: Feature importance + clear drivers (promo spikes, seasonality, stock gaps) Deliverables: Clean, documented dataset Fully commented notebook (retrainable pipeline) Forecasts (3/6/12 months) in CSV + visuals Dashboard (Power BI / Tableau or notebook visuals) Slide summary with insights & next steps I focus on reproducibility + business clarity, not just models. If you can share a small sample (1–2 files), I’ll validate structure and confirm the best model + timeline quickly. Let’s get you forecasts you can actually trust and act on. Best regards, Doan

@vankimt

My offer (€4,400 total / €55/hr -> €45/hr): • Data cleaning & feature engineering – €1,000  • ML modelling & tuning – €1,500 • Forecast dashboard (Jupyter/Plotly) – €1,100 • Documentation, slides & hand-off – €800 With my multidimensional background in logistics and supply chain along with a Master's degree in Logistics from TU Dortmund, I bring immense value to your sales forecasting project from the get-go. My experience with quantitative research and modelling at the Fraunhofer-Institut IML further attests to my proficiency to address complex data challenges just like yours. By meticulously exploring, cleaning, and engineering features that account for seasonality, promotions, and other business drivers, I can ensure meticulous handling of your sales data. In line with your preference, I am well-versed in implementing machine learning (ML) techniques, specifically tailored for time-series analysis just as you've mentioned. Likewise, I can still provide insights through a clean Jupyter notebook if visuals are embedded. While maintaining crucial error metrics to gauge performance at a glance(MAE and MAPE), my rich commentary will provide you with insights into the driving forces such as seasonal peaks, promotions spikes or stockouts - enabling you to make data-driven decisions swiftly and effectively. Additionally, I will ensure comprehensive documentation that would align with your future use of the cleaned and prepared dataset.

@Sarsimbek

Hello, I’d be a strong fit for this project because it combines exactly the kind of work I do well: data cleaning, exploratory analysis, forecasting logic, and turning raw historical sales data into practical decision support. I can help you with: * cleaning and structuring historical sales data from POS and e-commerce exports * identifying relevant drivers such as promotions, category effects, seasonality, and regional differences * building forecasting models that are appropriate for your data quality and business need * comparing baseline and ML approaches rather than forcing a complex model where a simpler one performs better * presenting the output in a way that is useful for planning, not just technically correct My focus would be on delivering forecasts that are: * transparent * measurable * easy to interpret * practical for inventory, sales, or management decisions I also pay close attention to validation, because forecasting is only useful when the model is tested properly and the assumptions are clear. I’d be glad to help turn your historical sales records into a reliable forecasting workflow with clear outputs and actionable insights. Best regards Sergej W.

@srinathm40

Before ML forecasting became mainstream, I was already building predictive models from raw transactional data — that's where my 13+ years in ML and software engineering come from. Your setup — POS exports, e-commerce data, promotions, regional outlets — is a well-structured forecasting problem. Here's my approach: I'd build a layered ML pipeline: Prophet for interpretable seasonality baselines, LightGBM as the primary model capturing promotions and regional patterns, and LSTM for deep sequential trends. The best performer — or an ensemble — becomes your production model, targeting under 10% MAPE on the hold-out set. Feature engineering is where forecasts win or lose. I'll engineer lag features, rolling averages, promotion flags, stockout detection, and regional encodings — everything that actually drives your numbers. Deliverables include cleaned data, fully commented notebook, 3/6/12-month CSV forecasts, a Power BI dashboard with MAE/MAPE KPIs, and a retraining script so your team can refresh the model as new data arrives. Could you share a sample export (even 2–3 weeks of anonymized data) so I can assess structure and confirm the approach before we lock in scope? Let's build something your team can actually use long-term.

@kazenesia

Hello, This is exactly the kind of project where a well-structured forecasting approach can directly improve business decisions. I can help you turn your historical sales data into a reliable, ML-driven forecasting system that not only predicts future performance but also explains what drives it. Here’s how I would approach it: • Data validation and cleaning, including handling missing values and outliers • Feature engineering to capture seasonality, promotions, and regional patterns • Model development using appropriate approaches (e.g., gradient boosting, Prophet, or hybrid methods) • Evaluation using MAE/MAPE with a proper hold-out validation setup • Clear visualization of forecasts (3, 6, 12 months) with business-friendly interpretation You’ll receive: • Reproducible code and a retrain-ready pipeline • Forecast outputs in both data and visual format • A clean dashboard or notebook with embedded insights • A concise summary highlighting key drivers and recommendations I also focus on making the model practical—so it can be updated easily as new data comes in, not just a one-time prediction. If helpful, I can review a sample of your dataset and suggest the most suitable modeling approach before we begin. Best regards, Bram

@fatmaasala7

I have experience in data analysis and machine learning, including regression and working with structured datasets. I can clean and explore your sales data, handle missing values, and build an accurate forecasting model using tools like Prophet or tree-based methods. I will evaluate performance using MAPE and provide clear visualizations and insights. I can also deliver a clean, well-documented notebook and dashboard for easy understanding and future use

@wennys8

Halo, saya tertarik dengan proyek peramalan penjualan yang Anda tawarkan. Saya memiliki kemampuan dalam analisis data dan machine learning menggunakan Python, termasuk eksplorasi data, pembersihan data, serta pembuatan model prediksi berbasis data historis. Untuk proyek ini, saya akan melakukan eksplorasi dan pembersihan data, menangani missing value dan outlier, serta melakukan feature engineering untuk menangkap pola musiman, promosi, dan faktor bisnis lainnya. Saya akan mengembangkan beberapa model seperti Prophet atau Gradient Boosting, kemudian membandingkan performanya menggunakan metrik seperti MAE dan MAPE untuk mendapatkan hasil terbaik. Output yang akan saya berikan meliputi dataset yang telah dibersihkan, notebook analisis yang rapi dan terstruktur, model yang dapat digunakan ulang, serta hasil prediksi untuk 3, 6, dan 12 bulan ke depan dalam bentuk file dan visualisasi. Saya juga akan menyertakan dokumentasi yang jelas agar model dapat digunakan kembali dengan mudah. Estimasi pengerjaan sekitar 2–3 minggu. Saya fleksibel dalam penyesuaian waktu kerja dan fokus pada efisiensi agar hasil optimal dengan biaya yang tetap terjangkau. Terima kasih.