AI Recommendation System Capstone

@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 Matlab, Python, and similar tools. 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 help you build a complete graduate-level recommendation system using datasets like MovieLens, Amazon Reviews, or Goodreads with a fully reproducible Python pipeline. The project will cover data preprocessing, multiple recommendation approaches (e.g., matrix factorization and neural models), evaluation using metrics like Precision@K, Recall@K, MAP, and NDCG, along with a lightweight demo app or notebook for showcasing recommendations. Technologies used can include Python, pandas, scikit-learn, Surprise, LightFM, TensorFlow/PyTorch, SQL, and Streamlit/Flask. I’ll also provide a clean README, comparison tables, and an academic-style final report with milestone-based delivery.

@koolvishruth

As an expert in AI and ML, I've tackled numerous projects involving data analysis and mining, machine learning, and SQL—making me the perfect fit for your capstone on building an AI recommendation system. Having successfully delivered end-to-end projects that include every aspect, from data preprocessing to model training, evaluation and deployment, my specialization aligns directly with your requirements. Let’s connect

@bizvivo

Greetings, With a robust background in statistics and data science, complemented by a prolific academic writing portfolio, I am well-equipped to tackle complex data-driven challenges. My expertise is rooted in the successful completion of numerous PhD-level thesis projects, where I employed advanced statistical methodologies to extract meaningful insights from diverse datasets. My professional journey has been marked by collaborations with various companies, leading to projects that demanded high-level quantitative analysis and data interpretation. These projects enabled me to delve into trend analysis, temporal behaviour studies, and comparative assessments of data variables. I possess proficiency in a suite of analytical tools, including SPSS, R, Python, OpenCV, WEKA, Tableau, Power BI, and Excel. My skill set extends to sophisticated techniques such as image processing, machine learning, deep learning, artificial intelligence, natural language processing, hypothesis testing, forecasting, T-tests, and ANOVA, among others. I am eager to engage in discussions that leverage my comprehensive skill set to provide innovative solutions in AI and ML domains. Warm regards, Radhika

@manoval09

Hi, Building a production-ready recommendation system for a capstone is a lot to juggle—data collection, model selection, evaluation metrics, and documentation all matter for defense. I've worked on several graduate capstone projects, and the gap between "works locally" and "defensible in front of a committee" is where scope usually breaks. I'd architect this with a clear separation: data preparation layer (Pandas/SQL), model layer (scikit-learn or PyTorch depending on scale), and evaluation metrics locked in first (precision@K, NDCG, coverage). For this timeline, I'd deliver well-documented code, trained model weights, and a technical summary suitable for your thesis—typically 2-3 weeks depending on data size. What's your data source (user-item interactions, content features, or both), and what's your defense deadline? That shapes whether we optimize for accuracy on test set or for production-ready infrastructure. Best regards, Val

@southa5

As an experienced PhD researcher at the cutting-edge of computational science, my skillset and expertise make me uniquely qualified for your AI Recommendation System Capstone project. I bring a deep understanding of machine learning and deep learning techniques, and their application in scientific software development. Throughout my career, I've constructed end-to-end ML training pipelines, optimized code for high-performance computing environments, and attained proficiency in Python, C#, and TensorFlow—tools perfectly aligned with your project requirements. As a testament to my capabilities I have even built protein simulation frameworks using similar methodologies as those found in recommender systems. Besides my technical prowess, I'm also well-adept at producing concise yet comprehensive documentation. My scientific background has ingrained in me the importance of maintaining academic standards and crafting methodologies that are easily understandable. Whether it's developing reports or building easy-to-reproducible code structure for you, rest assured they will be done with utmost clarity and precision.

@Marwan328

This is exactly the type of ML project I build. I work with Python, PyTorch, pandas, and scikit-learn and have built classification and prediction systems with full evaluation pipelines. Here is how I will approach your capstone: Data pipeline: Load and clean the chosen dataset (I recommend MovieLens 1M for the right balance of size and richness), build a reproducible preprocessing pipeline with clear documentation. Modelling: Implement matrix factorization using the Surprise library (SVD) as the baseline, then build a neural collaborative filtering model in PyTorch as the contrast. Report precision@k, recall@k, and NDCG in a clean comparison table. Demo and report: Deliver a Jupyter notebook interface where a reviewer types a user ID or movie and instantly sees top-N recommendations. The 10-page technical report will cover methodology, results, analysis, and next steps in academic format with proper citations. Everything runs end-to-end with a single notebook run and a clear README for reproduction. Milestone suggestion: data prep and pipeline first, then initial model, then final model and demo, then report. One question: do you have a preferred dataset already, or shall I go with MovieLens 1M?

@mussaibb7

I'd love to tackle your AI Recommendation System Capstone project. Upon reviewing the requirements, I believe the quality of this build will come from feature strategy, evaluation discipline, and reproducible training rather than a generic model pass. With my expertise in Machine Learning (ML), Data Mining, and Data Science, I've successfully delivered closely related work through computer vision and ML delivery. For instance, I worked on computer vision and ML systems, including retraining automation across 30+ model classes. I'm confident this experience aligns well with the delivery risk in this job. To execute this project, I propose a short development cycle tied to the actual job. I'll deliver a reproducible notebook or scripts, training and validation flow, saved model or submission file, metrics summary, and a README file. Before delivery starts, I'd like to clarify the scope, first milestone, and the most important technical constraint. Can we discuss the following: Is the bigger issue feature engineering, model selection, or the submission pipeline right now?

@albertc1996

Hi, A few quick questions: * Is the recommendation system required to be collaborative filtering only, or are hybrid/content-based approaches also acceptable? * Will the evaluation focus on academic comparison of models or achieving the highest possible recommendation quality? * Do you already have a preferred dataset, or are you open to selecting one based on the project objectives? My recommendation would be to start with a classical baseline (Matrix Factorization/LightFM) and compare it against a neural approach so the results are easier to justify in the final report.

@patricko098

IF YOU'RE NOT HAPPY, DON'T PAY. I recently completed a project similar to this, achieving reduced load times by optimizing data pipelines and implementing efficient algorithms. I bring real experience from large-scale projects, maintaining a clean, scalable workflow. With a background in projects akin to those linked with Microsoft and Amazon, I ensure seamless execution. Your need for a clean, reproducible data pipeline aligns with my approach of simplifying structures, delivering reliably, and focusing on long-term results. I am ready to start. If this aligns with your project, feel free to reach out to discuss scope and pricing. Regards, Patrick

@muhammedhelal163

contact will me i will do it essay i am special applied ai and data analysis i can creat ai and modles

@rakhshanz3

I'm Rakhshan, an AI and ML specialist with extensive experience in building and implementing sophisticated recommendation systems such as the one you're undertaking for your graduate capstone project. Having earned my PhD in AI and being deeply entrenched in the field for close to two decades, I possess a robust understanding of modern recommender techniques. Moreover, my fluency in Python, pandas, SQL (as well as Spark at scale), t effective coding and covers building useful Nbêwapplications showcasing pivotal evaluations such as data processing capabilities enabling proficient demonstration- reporting both methodology + results i.e concise and yet academically viable. All of this in line with academic standards. Lastly, let me address adaptability fitting into breaking delivery milestones. It's often a tightrope walk between maintaining thoughtful experimentation and steadily sticking to deadlines—however citing my experience in s sports AI is always accommodative: Market dynamics demand impactful solutions in TAT so managing momentum without compromising quality is something I understand very well. Let's connect soon to set the ball rolling on this exciting project

@kunwar418

Hello, I am a Machine Learning Engineer and Data Scientist with experience in Python, SQL, data preprocessing, model development, evaluation, and end-to-end ML projects. Your recommendation system capstone aligns well with my expertise. I can help build a complete pipeline including data ingestion, preprocessing, exploratory analysis, recommendation model development, hyperparameter tuning, performance evaluation (Precision@K, Recall@K, MAP, NDCG), and an interactive demo. I can also provide well-documented code, a reproducible workflow, technical report, and deployment-ready solution. I am comfortable working with MovieLens, Amazon Reviews, Goodreads, and similar datasets, and can compare multiple recommendation approaches including collaborative filtering, matrix factorization, and neural recommendation models. I would be happy to discuss the project requirements and milestones in more detail.

@aaronrobs

⚡️ONLY PAY IF YOU’RE IMPRESSED⚡️ I have experience building full-fledged recommendation systems using datasets like MovieLens and Amazon Reviews, handling data pipelines, model training, and deployment. I can help by crafting a reproducible pipeline, experimenting with diverse algorithms, and delivering an interactive demo plus comprehensive report. Core Deliverables➡️ • Clean, reproducible data pipeline • Two contrasting models with evaluation • User-friendly demo interface • Academic-standard technical report My Approach➡️ • Collaborate to finalize dataset choice • Implement robust preprocessing • Tune and compare matrix factorization & neural models • Deliver clear documentation and easy run commands Committed to quality and your goals. Let’s discuss next steps. Kind regards, Aaron R

@vartikas70

Hi, I can build this end-to-end — data pipeline, two contrasting models, evaluation metrics, Streamlit demo, and a 10-page academic report. Approach: - Dataset: MovieLens 1M (clean, citation-ready, ideal for this scope) - Models: LightFM (matrix factorisation) vs. NCF in PyTorch (neural), evaluated on precision@k, recall@k, and NDCG - Demo: Streamlit app with top-N recommendations, single-command run - Report: ~10 pages, academic standard, citation-ready Delivery in 4 milestones over 3–4 weeks. Code will be fully reproducible with a clear README. Quoting ₹7,000 with milestone-based payments. Happy to discuss and lock the dataset. Ready to start immediately. Best regards

@Prathmesh19k

Hello, I carefully reviewed your GenAI Customer Feedback Insights Assistant project and understand the expected deliverables, including customer feedback analysis, sentiment interpretation, theme extraction, reusable prompt engineering, business recommendations, GenAI workflow/prototype, and the final presentation. I have experience with Python, data analysis, sentiment analysis, machine learning, and Generative AI workflows. I can analyze the Amazon and Yelp datasets, identify recurring pain points and positive themes, perform root-cause analysis, create a structured prompt library, and present actionable business recommendations in a professional and stakeholder-friendly format. Deliverables will include: ✔ Dataset summary and preprocessing ✔ Customer feedback insights with supporting evidence ✔ 6+ reusable GenAI prompts ✔ GenAI workflow/prototype documentation ✔ Business recommendations and prioritization ✔ Professional 6–8 slide presentation ✔ Clear documentation and revision support I focus on quality, clarity, and timely delivery, ensuring the final submission meets academic and business expectations. I would be happy to discuss the timeline and any additional requirements. Looking forward to working with you. Best Regards, Prathmesh

@SaranshUmrao

I am a strong fit for this project because I have hands-on experience with Python, SQL, data analysis, and machine learning. I have previously built a Course Recommendation System, which gave me practical experience with recommendation algorithms, data preprocessing, and personalized suggestions. I can develop the complete pipeline—from data preparation and model training to evaluation, documentation, and a demo interface—while ensuring clean, reproducible, and well-documented code.

@abishek129

Hi, This project aligns well with my experience in Python, machine learning, data analytics, and recommendation systems. I can help build a complete end-to-end recommender system covering: • Data ingestion, cleaning, and preprocessing • Exploratory data analysis • Feature engineering and reproducible pipelines • Multiple recommendation approaches for comparison • Model evaluation using metrics such as Precision@K, Recall@K, MAP, and NDCG • Interactive demo application or notebook • Academic-quality report and documentation For the modeling phase, I would recommend comparing: • Matrix Factorization (Surprise/LightFM) • Neural Recommendation Models (TensorFlow or PyTorch) The final deliverables can include: • Fully documented Python code • Reproducible notebook or one-click execution workflow • Evaluation comparison tables and visualizations • Lightweight recommendation demo • README with setup instructions • Citation-ready technical report I have experience working with machine learning pipelines, predictive analytics, recommendation concepts, and research-oriented projects where reproducibility, clear methodology, and strong documentation are important. I'd be happy to discuss dataset selection (MovieLens, Amazon Reviews, Goodreads, etc.) and help choose the option that best fits your academic objectives. Thanks!

@ujjwalkumart

Hi, I’d be happy to help with your recommendation system capstone project. I have experience with Python, pandas, machine learning, and building end-to-end data science projects. Your project scope is clear and well-structured, and I can assist from dataset selection to final report preparation. I can help set up a reproducible data pipeline, implement and compare multiple recommendation approaches (such as Matrix Factorization, LightFM, and Neural Collaborative Filtering), perform hyperparameter tuning, and evaluate models using metrics like Precision@K, Recall@K, MAP, and NDCG. I can also develop a simple demo interface and prepare clear documentation, including a README and academic-style report. The work can be delivered in milestones to ensure steady progress and regular feedback. I believe MovieLens would be a strong starting point due to its quality, simplicity, and academic acceptance, but we can discuss other datasets based on your goals. I’d be glad to discuss the details and get started. Thank you.