Part-Time Sales Forecast Analyst

@besho87

Hello, I’d be happy to build a reliable sales forecasting model from your CSV/Postgres data. I have experience with Python, pandas, SQL, scikit-learn, statistical analysis, and time-series forecasting. I can compare models such as Prophet and other forecasting approaches against a naïve baseline and select the best-performing method using a held-out test set. I’ll provide: * Forecasts for 3, 6, and 12 months with 95% confidence bounds. * Fully documented Python code and environment file. * SQL data extraction and reproducible workflow. * Model validation against the naïve baseline. * A concise report explaining key sales drivers and forecast results. * A short walkthrough showing you how to update/retrain the model. I’ll focus on accuracy, reproducibility, and actionable business insights. I’m ready to start immediately. Best regards.

@iterativeTech

Using data to power better decisions and maximize returns is my passion, and your Part-Time Sales Forecast Analyst project aligns perfectly with my skill set. My vast experience in AI/ML development, coupled with strong proficiency in Python (including pandas, scikit-learn, Prophet), SQL, and statistical analysis, gives me the capability to draw actionable insights from your cleaned customer data in CSV and Postgres. I've also excelled in transforming such information into tangible business outcomes, like improving operational efficiencies and enabling smarter decision-making. Over the years, I have successfully engineered precise forecasting models that outperform baseline methods by significant margins--something essential for your project's success. I can deliver not only a well-documented forecasting model but also the driving factors behind the projections along with measures of uncertainty for each period. Further ensuring that our collaboration is seamless, I'm comfortable working on your machine without any modification to the provided environments.

@gowrik16

Hi, I can help turn your historical customer sales data into reliable monthly and quarterly forecasts with a reproducible Python-based forecasting workflow. My approach would be: • SQL & data preparation – query the required sales history from PostgreSQL and prepare the modelling dataset. • Time-series analysis – identify trend, seasonality and patterns influencing sales. • Model development – compare Prophet, ARIMA and SARIMA approaches and select the best-performing model. • Model validation – use a held-out test set and compare performance against a naïve last-period baseline using suitable error metrics. • Forecasting – generate 3, 6 and 12-month forecasts with 95% confidence intervals and clearly communicate the key drivers. • Documentation & walkthrough – provide reproducible code/environment details, a concise report and a short walkthrough showing how to update or retrain the model. My preferred stack is Python (Pandas, NumPy, Prophet, Statsmodels and Scikit-learn) + SQL/PostgreSQL. I have hands-on experience with time-series forecasting using Prophet, ARIMA and SARIMA, along with Python-based data analysis and SQL. I have completed similar sales forecasting and predictive analysis projects before. If needed, I can share a relevant completed project for your reference so you can review my approach and technical work. Regards, K. Gowri

@novatech2210

⚠️ IF YOU'RE NOT HAPPY YOU DON'T PAY ⚠️ I think we're a strong fit for your project. I specialize in Python, SQL, Statistics, Statistical Analysis. For this brief (I’m looking to bring in a part-time data analyst who can apply predictive analysis techniques to my) I would isolate the bottleneck, confirm acceptance criteria, and ship a clean, measurable fix you can verify in staging before it hits production. I'd keep the architecture simple, secure, responsive, and easy for you to manage after handover. Multiple 4.0-rated reviews on Freelancer (16 total), payment verified. I can start against a 7-day delivery window. I'd love to chat about your project! The worst that can happen is you walk away with a free consultation. Regards, N0VATECH

@khadijatul15

Hi, I've reviewed your project, "Part-Time Sales Forecast Analyst", and I understand what you're looking to achieve. Based on the requirements in your project description, my Python, SQL, Statistics, Statistical Analysis, Data Analysis, Predictive Analytics experience aligns well with the work you need. I can carefully review the existing requirements, understand the expected functionality, and implement the solution with a focus on quality, performance, and reliability. Project Requirements: I’m looking to bring in a part-time data analyst who can apply predictive analysis techniques to my customer data so I can forecast monthly and quarterly sales with greater accuracy. The raw information is already cleaned and stored in CSV and a Postgres database; what I need now is someone who can turn that history into forward-looking insights. You’ll have flexible hours—as long as reliable forecasts and clear, actionable summaries arrive on the agreed schedule. I’m comfortable with you working in Python (pandas, scikit-learn, Prophet) or R (tidyverse, caret, forecast); SQL proficiency is e I’ll make sure the work is handled professionally, with clear communication throughout the project and attention to the details mentioned in your requirements. I’m ready to discuss the project and get started. Best Regards, Khadija Tul Kubra

@pawankrishna297

Hi, I’m a Data Analyst experienced in Python, Pandas, SQL, statistical analysis, and predictive analytics. I can help turn your historical customer/sales data into reliable 3, 6, and 12-month forecasts. My preferred stack is Python with Pandas, scikit-learn, Prophet/statsmodels, and SQL for extracting and preparing the PostgreSQL data. My approach will be: • Analyze trends, seasonality, and key sales drivers. • Build a naïve baseline and multiple forecasting models. • Use time-based train/test validation to avoid data leakage. • Compare models using MAE, RMSE, and MAPE. • Select a model only after demonstrating improvement over the baseline. • Produce forecasts with 95% confidence intervals. • Deliver clean, documented code, requirements/environment file, and a concise report. • Provide a short Loom/Zoom walkthrough showing how to run and retrain the model. I have hands-on experience analyzing sales data with Python and SQL, including revenue trends, customer/sponsor segmentation, churn analysis, and KPI reporting. I focus on producing reproducible, practical analysis—not just predictions. I can start by reviewing your CSV/PostgreSQL structure and establishing the baseline model.

@xcubeits

As an acclaimed PHP and Full-Stack Developer with over 10 years of hands-on development experience, I believe my skills are highly transferable to your data analysis project. I have worked extensively with **SQL databases** including MySQL and PostgreSQL, which aligns perfectly with your project's core requirement of extracting valuable insights from CSV and Postgres database. Additionally, I am adept at **Python**, particularly in the *pandas* and *scikit-learn* libraries that you've mentioned. My expertise extends beyond simply writing codes to providing **smart solutions** to real-world problems. As a senior-level professional, I have had the opportunity to work on projects across diverse domains ranging from **SaaS platforms** to **CRM & ERP systems**. These past experiences have endowed me with a deep understanding of how data influences various business processes, making me well-equipped for this sales forecasting task. In conclusion, I offer a unique combination of technical expertise in Python and SQL databases, alongside a broad understanding of business applications and practices. My approach, always focused on delivering clean, secure, scalable and high-performing solutions, makes me the ideal candidate to create an accurate forecasting model that provides valuable forward-looking insights for your business.

@jayantb24

Hello, I’m a Data Analyst with 6 months of hands-on internship experience in Python, SQL, Excel, data cleaning, EDA, and business analysis. For this project, I can work with your CSV/PostgreSQL data, use SQL to extract the required datasets, and build a structured sales forecasting workflow in Python. I am comfortable working with Pandas and data visualization, and I can evaluate the forecasting model against a simple baseline before finalizing the approach. I can provide: • Clean and well-documented Python code • Forecasts for 3, 6, and 12 months • Model evaluation and comparison with a naive baseline • Clear analysis of the factors influencing sales • A concise report with understandable results • A walkthrough explaining how to run and update the model I would be happy to discuss your data structure and forecasting requirements before starting. Regards, Jayant Bharadwaj Data Analyst

@abhinandd31

Hi, This sounds like a good fit. I’d build the solution in **Python using Nixtla’s StatsForecast + MLForecast**, with Prophet as a benchmark. For monthly sales data, I’d compare **Seasonal Naive, AutoETS, AutoARIMA, AutoTheta, Prophet**, and **LightGBM** when useful drivers such as pricing, promotions, marketing spend, or customer activity are available. The final model would be selected based on actual performance rather than choosing one upfront. I’d first extract and reconcile the PostgreSQL/CSV data and exclude incomplete periods. Then I’d use **rolling-origin backtesting** to test how each model would have performed historically. Performance would be measured against a seasonal-naive baseline using **MASE**, with a **Diebold-Mariano test** to determine whether the improvement is statistically significant. Deliverables would include **3, 6, and 12-month forecasts with 95% intervals**, quarterly projections, key forecast drivers, documented code, pinned dependencies, SQL queries, and a simple end-to-end retraining workflow. I’d also provide the Loom/Zoom walkthrough showing how to update and retrain the model. Estimated timeline: **~3 weeks part-time**. Before starting, I’d confirm the amount of historical data, major business changes, known future promotions/pricing changes, and whether forecasts are needed by total sales, product, region, or customer segment.

@Pratik0620

Hi, I can work on this using Python, PostgreSQL and a reproducible time-series forecasting pipeline. I would first analyse the historical sales series for trend, seasonality and relevant customer-level drivers, then establish naïve/seasonal baselines before evaluating models such as SARIMA/SARIMAX, Prophet and feature-based ML where appropriate. I would use time-based validation rather than random train/test splitting and compare the models using suitable forecasting metrics. The final solution will generate 3, 6 and 12-month forecasts with uncertainty intervals, along with documented Python/SQL code, environment setup and a concise report explaining the important drivers and results. My background is in Python-based data analysis and machine learning using Pandas, scikit-learn, SQL and statistical modelling. While I would first need to inspect your dataset before selecting the final forecasting method, I can build the workflow so it is easy to rerun and retrain as new sales data becomes available. I can also provide a walkthrough showing how to update the data and generate future forecasts independently.

@thefavs0

Hi, I handle data entry projects accurately and at speed — and wherever possible I automate the process in Python to eliminate human error. For this project I would: • Start with a detailed scope discussion to make sure we're 100% aligned • Deliver working, tested code — not something that only runs once • Keep you updated at each stage • Be available for questions and adjustments after delivery Relevant experience for this specific project: Python, Data entry, SQL / database I can start today. What's your preferred timeline? Best, Favour

@faizulm8

Hi, I’m interested in your Sales Forecast Analyst project. I have knowledge of Python, SQL, data analysis, and machine learning fundamentals. I can work with CSV and database data using pandas and SQL and build forecasting models for 3, 6, and 12-month sales projections. I will provide clean code, clear documentation, and an easy-to-understand summary of the results. I’m committed to delivering accurate work on time.

@bhargav922002

Hello, I’m bharghav, and I bring over 10 years of experience in matching job skills with project requirements, particularly focused on data analysis and predictive modeling. My expertise in Python and SQL aligns perfectly with your needs for a part-time sales forecast analyst. I understand you’re looking for someone to transform your cleaned customer data into actionable forecasts for monthly and quarterly sales. I will utilize Python libraries such as pandas and scikit-learn to build a robust forecasting model along with SQL for precise data extraction. The deliverables will include a well-documented model, a report detailing the drivers behind the forecasts, and a short video walkthrough to empower you to update and retrain the model independently. Let's start a chat to discuss your project requirements in detail and clarify any questions you may have. I’m eager to ensure that we’re aligned on your expectations and set a timeline for deliverables. Best regards, bhargav922002

@dmahendra1

Hello, I’m interested in your Part-Time Sales Forecast Analyst project. I am a Computer Science graduate with hands-on experience in Python, Pandas, SQL, MySQL, data analysis, and data visualization. For this project, I would use Python and SQL to work with your CSV/PostgreSQL data, prepare the required time-series dataset, establish a suitable baseline, and develop and evaluate a forecasting model. I am comfortable working with Pandas and scikit-learn and can also work with forecasting libraries such as Prophet where appropriate. My planned approach would be: 1. Extract and validate the required sales data using SQL. 2. Analyze historical sales trends and seasonality. 3. Create a naïve baseline for comparison. 4. Build and evaluate an appropriate forecasting model using a held-out test set. 5. Generate 3-, 6-, and 12-month forecasts with 95% confidence intervals. 6. Prepare clear charts and a concise report explaining the results and important drivers. 7. Provide well-documented Python code and an environment file so the workflow can be reproduced. 8. Provide a short walkthrough explaining how to run and retrain the model. I focus on clean, documented, reproducible work and clear communication. I would be happy to review a sample of your data and existing PostgreSQL structure before finalizing the forecasting approach. Thank you for considering my proposal. Best regards, D. Mahendra

@bohdanmelnykua

Hi! I've built forecasting pipelines like this before - pandas/scikit-learn for the modeling, direct Postgres queries for pulling the history. Happy to start with a baseline model against your existing data to validate the approach before tuning further. Quick question: any preference between a simple seasonal baseline vs Prophet for the first pass, or should I decide based on what fits your data best?

@bijayinid5

I have worked on forecasting models. I have 7 years experience in AI and ML. I can work on this. Also i have worked on customer data.

@IAmMaryViD

Hello, I'm a Data Scientist with a Master's degree in Data Science Engineering, specializing in predictive modeling and time-series forecasting. For this project, I'd use Python (pandas, scikit-learn, Prophet) paired with SQL for pulling data directly from your Postgres database — this keeps everything reproducible and easy for you to retrain later. My approach: Pull and validate the relevant history from Postgres/CSV via SQL, checking for seasonality, trend, and any gaps. Build a baseline (naïve last-period model) to establish the benchmark to beat. Develop and tune a forecasting model (Prophet for seasonality-aware trends, with scikit-learn regression models as a comparison), validated on a held-out test set to confirm it statistically outperforms the baseline. Generate forecasts for the 3, 6, and 12-month horizons with 95% confidence intervals. Deliver clean, documented code with an environment file, a short report explaining the key drivers behind the projections, and a Loom/Zoom walkthrough so you can update or retrain the model independently. I've built end-to-end ML pipelines before with automated feature engineering and hyperparameter optimization (Optuna) across multiple model types, so I'm comfortable ensuring the final model is both accurate and easy to maintain. I'm available to start on a flexible, part-time basis and can share more detail on my modeling process once we connect. Looking forward to discussing further. Best regards, Marie Victoire

@marisaparyasto

Hi, I’d be happy to build the sales forecasting solution for your customer data. I’m an expert in **Python, SQL, and machine learning**, with strong experience in data analysis, predictive modeling, and reproducible workflows. My approach would be to: * Extract and prepare the required data directly from PostgreSQL. * Establish naïve and seasonal-naïve baselines. * Analyze trends, seasonality, and relevant sales drivers. * Compare suitable forecasting approaches such as **ETS/ARIMA, Prophet, and ML-based models**. * Use proper **time-series backtesting/rolling validation** rather than random train/test splits. * Select the best-performing model based on out-of-sample results. * Produce **3, 6, and 12-month forecasts with 95% prediction intervals**. * Deliver well-documented Python code, an environment file, a concise report, and a Loom/Zoom walkthrough explaining how to retrain the model. I’ll focus on making the entire pipeline reproducible and able to run end-to-end on your machine without modification. For libraries, I prefer the Python ecosystem, particularly **pandas, statsmodels, scikit-learn, and Prophet**, while choosing the final methodology based on the characteristics of your data rather than assuming one model will be best. I’d be glad to share a representative sales/demand forecasting example as well. **Bid: ₹400/hour** Best regards, Marisa

@arijitg49

Hi, I’m interested in helping you build a reliable sales forecasting solution from your existing CSV and PostgreSQL data. I have experience with Python, pandas, scikit-learn, SQL, statistical analysis, and predictive modeling, and I can build a complete, reproducible forecasting workflow. I would compare multiple approaches such as Prophet, statistical time-series models, and ML-based forecasting rather than relying on a single model, then select the best performer against a naïve baseline using proper time-series validation. The final model will provide 3, 6, and 12-month forecasts with 95% confidence intervals and clear analysis of the factors influencing the projections. I’ll provide well-documented code, an environment/requirements file, and a concise report explaining the methodology and results. I can also provide a short walkthrough showing how you can retrain and update the model yourself. I’ll make sure the complete workflow runs end-to-end on your machine without manual code changes. I’d be happy to discuss your dataset and share an example approach for building a sales/demand forecasting model.