Predict Customer Behavior From Sales -- 2

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Python, and similar tools. I have worked with pytorch, and tensorflow to develop DL models, .I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@glanegonsalves

Hey there Glane here, I can build the complete customer purchase prediction workflow in Python using Pandas and Scikit-learn, modelling both next purchase probability and likely product category. I’ll engineer behavioural features such as recency, frequency, monetary value, purchase intervals, and category history, then compare suitable models using a properly separated held-out test set and metrics such as ROC-AUC, precision/recall, F1, and category level performance. I’ll provide interpretable feature importance/explanations, actionable insights for marketing and inventory teams, clear visualisations, and a concise report or slide deck, with all notebooks, code, and intermediate outputs organised in a reproducible repository so your analyst can rerun the workflow on future sales data.

@HanyasSalem

Hi there, With my background in both medicine and data analysis, I can bring a unique perspective to your customer behavior prediction project. My experience in biostatistics and machine learning gives me the expertise necessary to handle statistical analyses on large datasets like yours and provide thorough results. I am comfortable using Python, and have a strong command over their respective libraries like pandas, scikit-learn so you can trust that the model created will be of high quality. Moreover, my ability to effectively communicate complex findings in a clear and concise manner is a key skill I bring to the table. We'll work together to translate the results of the predictive model into actionable insights for your marketing and inventory teams. Understanding the importance of reproducibility, I ensure that all code, notebooks, intermediate data files are well-documented and organized in a shared repository for easy rerun. I usually use GitHub as my favourite repository hosting site. So, you can monitor the progress and provide suggestions. In conclusion, not only do I understand the technicalities of your project requirements, but I'm also accustomed to delivering thorough and actionable results. With my skills in both ML, Python, Statistics, I believe I would be an ideal fit for this project. Let's get started!

@imranahmadquresh

Hi, I understand you need Machine Learning Expert using Python to Predict Customer Behavior From Sales. I offer my services for this project. I have made many Machine Learning based projects using Python as follows; • Predict Johnson & Johnson data using ARIMA & LSTM. • Stock Price Prediction of bank data & Amazon data using ARIMA & LSTM. • Handwritten Digit Recognition using Fourier response & SVM polynomial. • Classification of CIFAR-10 using different NN models. • Classification of Sentiment of Movie Review using Logistic Regression. • Classification of London Fire Brigade incidents 2019-2022 data using Decision Tree. • IOT Attacks Prediction using SVM, Decision Tree & Random Forest. • Prediction extent of disease of ECG data using Random Forest & SVM. • Time Series Price Forecasting using Random Forest. • Classification of dementia disease using XGboost & Logistic Regression. • Classification of Iris & Breast cancer using SVM, Decision Tree, NN & Naive Bayes. • Classification of Muffin & Cupcake ingredient data using SVM. • Prediction of Solar Radiance using SVM & Bayesian Ridge. • Regression of Boston & Diabetes using Decision Tree, KNN & NN. • Regression of Wine quality using Random Forest. • Clustering fingerprint images using K-Means. • Data analysis of Wales Employment data using PCA, Correlation, K-means & Hierarchical Clustering. • Classification & Hypothesis Testing Hotel Booking Cancellation Prediction. I ensure to complete your project efficiently and on time.

@singhashishb2

Hi,I can build a reproducible customer purchase prediction pipeline from your historical sales CSV. I’ll handle the complete workflow: data exploration → feature engineering → customer segmentation → next-purchase probability → product-category prediction → model evaluation → business insights. I’ll use Python with Pandas and Scikit-learn, with models selected based on the data and validated on a proper held-out test set. I’ll provide metrics such as AUC, precision/recall and accuracy where appropriate, along with feature importance/explainability. Deliverables will include: * Reproducible Python code and notebook * Trained models and prediction output for each customer * Feature importance/explanations * Visualizations using Matplotlib where useful * Concise report/slide deck with actionable marketing and inventory recommendations * README and all required files in a structured repository I’ll keep the workflow clean and rerunnable so your in-house analyst can easily update it with new sales data. I’m ready to review the CSV schema and start the analysis.

@mike3608

Excited to offer my expertise for your predictive analysis project! I'm well-versed in Python, utilizing pandas and scikit-learn to build reliable and reproducible models. I'll deliver clear feature insights and actionable results in a straightforward presentation, ensuring all findings are easily understandable for your marketing and inventory teams. Expect thorough documentation, performance metrics validation, and an organized shared repository for seamless integration with your team. Looking forward to contributing meaningful insights to your sales strategy!

@thiagofoz87

Your dataset already being cleaned is a good starting point because it allows the work to focus immediately on behavioral patterns, feature engineering, and model quality instead of data correction. Based on your description, I would structure the analysis in four stages: exploratory analysis and customer segmentation, feature generation from purchase history, predictive modeling for repurchase probability and category prediction, and finally interpretation/reporting for business teams. The workflow would be fully reproducible in Python using pandas and scikit-learn, with notebooks and modular scripts organized so your internal analyst can rerun or extend the pipeline later. For the predictive layer, I would evaluate models appropriate for transactional behavior data such as gradient boosting or ensemble methods, then validate them using a held-out test set with metrics like ROC-AUC, precision/recall, and calibration depending on class balance. I would also include interpretable outputs such as feature importance and customer-level scoring so marketing and inventory teams can understand not only the prediction itself, but the drivers behind it. If useful, I can add visual dashboards or charts with Matplotlib or Power BI-oriented exports. Deliverables would include the complete repository, notebooks, intermediate artifacts, documented workflow, and a concise executive report/slides translating the technical findings into operational recommendations.

@Marwan328

HI !, I am keen to undertake your project to predict customer behavior from historical sales data. With extensive expertise in Python-based machine learning, data analysis, and visualization, I will develop a robust, reproducible predictive model using pandas and scikit-learn, focused on forecasting next purchase probability and product category per customer. My approach includes comprehensive exploratory data analysis and feature engineering to enhance model accuracy, followed by generating clear feature importance insights to identify key drivers behind customer purchases. I will provide performance metrics such as AUC or accuracy on a held out test set to validate model quality objectively. Deliverables will include well-documented code and notebooks in a shared repository for seamless reruns, alongside a clear, concise slide deck translating technical results into actionable insights for your marketing and inventory teams. If desired, I can also incorporate visualizations with Matplotlib or Power BI to support deeper understanding. I am ready to collaborate closely, addressing schema questions promptly to expedite analysis. My priority is delivering high-quality, transparent predictive analytics that empower your business decisions. Best regards, Marwan

@gunjan1711

Hi, I have hands-on experience with Python, Pandas, scikit-learn, statistics, machine learning, and predictive analytics, with a focus on turning customer data into actionable insights. - I can build a reproducible customer-purchase prediction workflow covering repeat-purchase probability and likely product category. - My experience includes data preparation, feature engineering, predictive modeling, model evaluation, and customer behavior analysis using Python. - I can provide clear feature-importance outputs so the business can understand the factors influencing predictions. - I can validate the model using an appropriate held-out test approach and report relevant performance metrics. - I can translate the results into practical recommendations for marketing and inventory teams, supported by clear visualizations where useful. - I’ll keep the notebook/code structured and documented so your analyst can rerun the complete workflow on future data. Reference work is available in my profile.

@RKhakhula

Hi There, I will analyze your historical sales data and deliver a complete predictive machine learning pipeline tailored to anticipate customer purchase behavior. Key deliverables: Reproducible Python model (pandas, scikit-learn) to predict next purchase probability and target product category. Detailed feature importance analysis highlighting the primary behavioral drivers. Rigorous model validation evaluated on a held-out test set using AUC and accuracy metrics. Cleanly organized shared repository with documented notebooks for your in-house analyst. Concise slide deck with actionable recommendations for marketing campaign targeting and inventory management. With deep experience in data engineering, machine learning, and business analytics, I ensure smooth execution and clear strategic insights. Let's contact to discuss details. Solutin Vector Roman Khakhula

@engemadmohamed

Hello! I'm Emad, a Software Engineer with a strong foundation in algorithms and Python-based predictive analytics. I have carefully reviewed your requirements, and I am ready to turn your historical sales CSV into actionable intelligence. I will build a reproducible Machine Learning pipeline using Python (pandas and scikit-learn) to predict the next purchase probability and product category for each customer. Here is my action plan: Exploratory Data Analysis (EDA): Quick dive into your schema to understand the cleaned data. Model Building: Training predictive models and evaluating them on a held-out test set using metrics like AUC and Accuracy. Feature Extraction: Clearly highlighting which factors drive the predictions so your marketing team can take action. Deliverables: I will provide a clean, well-documented shared repository with all notebooks, along with a concise slide deck translating technical results into plain-language business insights. I am ready to look at your schema and start immediately. Let's chat! Best regards, Emad

@Vaibhavtimba

Title: Predictive Customer Purchase Analysis in Python Hello, I can help you build a reproducible customer purchase prediction workflow using Python, pandas, and scikit-learn. I will: - Analyze and prepare the historical CSV sales data. - Build a model to predict the probability of a customer's next purchase. - Predict the likely next product category. - Provide feature importance/explanatory results. - Evaluate the models using suitable held-out test metrics such as AUC, accuracy, precision/recall, or F1 where appropriate. - Create clear visualizations and plain-language business insights. - Prepare a concise report/slide deck with recommendations for marketing and inventory teams. - Deliver the complete Python code, notebooks, and required intermediate files so your analyst can reproduce the workflow. I can work within the 30-hour requirement and complete the work within 7 days. I am ready to review the CSV schema and start with exploratory analysis and modelling. Thank you.

@husainl7

CSE AI ML ENGINEER 2 years of experience, 50 + projects in AI with 5 projects currently being actively used in industry

@ziadm86

I specialize in building reproducible predictive models that help businesses understand what their customers will buy next, when they'll buy it, and how likely they are to return. Your project is a perfect fit for my skill set. what I will deliver: -Python (pandas, scikit-learn) pipeline predicting next purchase probability and likely product category per customer -Feature importance analysis — you'll know exactly which factors drive each prediction -Plain-language insights and actionable recommendations for your marketing and inventory teams -All notebooks, scripts, and intermediate files in a shared repo your analyst can rerun end-to-end -Rigorous metrics (AUC, precision-recall, or best-fit measure) on a held-out test set Why Me? -Reproducibility first: Every step is scripted, versioned, and documented — no black boxes -Business translation: I don't just build models; I explain what they mean and what to do with them -Fast turnaround: I'm ready to start immediately once schema questions are clarified

@winston052790

Hello, I hope this message finds you well. I am writing to express my interest in the position of Predicting Customer Behavior From Sales. With my expertise in Python, Software Architecture, Statistics, Machine Learning, Data Science, Data Visualization, and Predictive Analytics, I am confident in my ability to deliver the results you are looking for. I am eager to create a reproducible model using Python or R that accurately predicts customer behavior and product category preferences. I will provide clear feature importance and comprehensive insights in a concise report. Additionally, I will ensure the code and all related files are easily accessible for future use. I am ready to jump into the analysis and modelling process, and am happy to answer any questions you may have. If needed, I can also incorporate additional visualization tools such as Tableau, Power BI, or Matplotlib. Thank you for considering my application. I look forward to discussing how I can contribute to your team. Sincerely, Winston Thank you.

@mahnooramir94

Hi, I'd be glad to help with this predictive analysis project. I've worked on similar data science projects before, including a sales analytics dashboard (Adventure Works dataset) covering customer segmentation and purchase trends, and a predictive maintenance alert system using classification models with feature importance analysis , Here's how I'd handle it: Data prep: I'll go through your CSV, check for any hidden issues beyond the initial cleaning, and build useful features like how recently and often a customer buys, along with their category history. Modeling: Using Python with pandas and scikit-learn, I'll build models to predict whether a customer is likely to buy again and which category they're likely to buy from. Explaining the results: I'll show which factors are driving each prediction, so it's not a black box for your team. Testing it properly: I'll check the model's performance using AUC, accuracy, or whatever metric fits best, on data it hasn't seen before, so you know the results are reliable. What you'll get: All the code and notebooks in a shared repo, so your analyst can rerun it anytime. A short, clear report or slide deck explaining the findings in plain language, with practical suggestions for marketing and inventory teams. I can also add Power BI or Matplotlib visuals if that would help. Happy to hop on a quick call first to go over your data schema so I can get straight into the work without delays. Looking forward to it. Best, Mahnoor

@warrick1718

This project immediately caught my attention because it is exactly the type of work I do best. Predicting customer behavior through a clean and user-friendly model aligns perfectly with my expertise. I understand the need for a reproducible model in Python or R, along with clear feature importance to inform your marketing strategies. While I am new to freelancer, I have tons of experience and have done other projects off-site, including predictive analytics and customer behavior modeling. My background ensures I can deliver a seamless analysis with a concise report and performance metrics as specified. If this sounds like what you're looking for, I'd love to hear more about your project. Regards, Warrick Van Eeden

@Mario0Andrei1

Hi, I'd like to apply for this project. I have hands-on experience with Python, pandas, and scikit-learn, and I've worked with predictive modeling and machine learning during my university coursework (MATLAB/Simulink for predictions and control systems). I'm comfortable building reproducible ML pipelines, evaluating models with metrics like AUC and accuracy, and presenting results clearly through visualizations and reports. I can deliver: a clean Python notebook with the full workflow, feature importance output, performance metrics on a held-out test set, and a concise report translating the findings into plain-language insights. All code and files delivered in a shared repo so your analyst can rerun everything. Happy to start as soon as you share the data schema. Best regards, Mario

@risvanthkm

Hi, I’m a second-year student at NIT Trichy and an AI/ML Engineer at Delta Force, the institute’s official technical organization. I’ve been working extensively with Python, pandas, scikit-learn, PyTorch, data analysis, and machine learning. Your project is a great match for my experience. I can handle the complete workflow—from exploring the sales data and engineering customer-level features to training, evaluating, and explaining the prediction models. I’ll focus on proper validation and avoiding data leakage, especially since this is historical purchase data. The final workflow will be reproducible and clearly documented so your in-house analyst can rerun it. I can deliver: - Customer purchase probability predictions - Likely next product category - Feature engineering and exploratory analysis - Model evaluation on held-out data - Feature importance / model explanations - Visualizations and actionable insights - Clean Python code and notebooks - A concise report with recommendations I’m comfortable working with unfamiliar datasets and choosing the modeling approach based on the characteristics of the data rather than forcing a particular algorithm. I’d be glad to take this up and deliver a clean, practical solution. Risvanth K M NIT Trichy | Delta Force | AI/ML Engineer