AI & Data Science Expert Needed for Data Analysis and Machine Learning

@WycOj

As a Biostatistician, Data Analyst, and Researcher with over 7 years of experience, I am confident in my ability to handle your AI and Data Science project. I have completed numerous projects involving data analysis, machine learning, and deep learning utilizing Python, [Add another software you want to include]. I specialize in Data Cleaning and Preprocessing, Exploratory Data Analysis (EDA), as well as developing and evaluating Machine Learning models. My proficiency with libraries such as Pandas, NumPy, and Scikit-learn enables me to clean complex datasets, identify important patterns, and create meaningful visualizations. Moreover, I have also utilized TensorFlow and PyTorch extensively for implementing Deep Learning models when required. I can assure you that my work is not only rigorous but also transparent which means you can always expect clear code with detailed methodology explanations. This transparency extends into my reports where I convert analytical results into practical business insights. All my outputs are reproducible ensuring the longevity of your project results. In addition to these, I also have ample experience with NLP, Computer Vision, Time Series Analysis, Feature Engineering and Model Optimization among others – which would be an added bonus for your project. My availability is flexible to suit your needs and negotiable pricing will be based on the scope of the task at hand. Let's collaborate and breathe life into your data!

@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

@PINESCRIPTERR

Hi, I can handle end-to-end data science work in Python, from data cleaning, preprocessing and EDA through statistical analysis, feature engineering, visualization and machine learning model development. I can work with classification, regression, clustering and deep learning using Pandas, NumPy, Scikit-learn and TensorFlow/PyTorch, with reproducible code, proper evaluation, error analysis and clear explanations of the results. Questione 1: What type of datasets and prediction or analysis problems will be the main focus of the first project? Questione 2: Do you already have a preferred modeling workflow and evaluation metrics, or should these be defined during the initial analysis? Regards, Houssame.

@HanyasSalem

Hi there, As an accomplished AI and data science expert, I'm ready to help you transform your raw data into rich insights and powerful predictive models. You're looking for someone comfortable with real-world datasets and transforming them into practical AI solutions; this is precisely what I've been doing over the years. With hands-on experience in Classification, Regression, Clustering, and even Deep Learning when required, I can tackle any machine learning problem that comes my way. My problem-solving skills coupled with my ability to express complex ideas in clear terms make me adept at converting analytical results into actionable business insights - a skillset that aligns perfectly with your project expectations. In terms of technical know-how, I breathe Python. With undeniable expertise in Pandas, NumPy, Scikit-learn as well as Matplotlib/Seaborn, I bring advanced knowledge in exploratory data analysis, data cleaning/preprocessing, model evaluation, error analysis, and data visualization to the table. Additionally, my familiarity with TensorFlow and PyTorch gives me an edge for incorporating deep learning models when required. All in all, I am confident I have the skills you're seeking for your project

@arslanshahid386

Hi, I understand you're seeking an AI & Data Science expert to handle data analysis, machine learning, and AI model development. Your emphasis on working with real-world datasets to derive meaningful insights and predictive models is clear. A key challenge in this project is ensuring data quality and integrity during the data cleaning and preprocessing phase. This step is crucial as it directly impacts the accuracy of the predictive models. My approach would involve a thorough initial assessment of the dataset to identify any anomalies or missing values, followed by a structured preprocessing strategy to ensure stability and reliability of the data analysis. With extensive experience in Python and data processing, I've successfully managed similar projects, turning complex datasets into actionable insights and robust AI solutions. Q1 – Could you specify the volume and nature of the dataset involved? Q2 – Are there any specific machine learning algorithms or models you prefer for this project? Q3 – What are the primary objectives or KPIs you aim to achieve through this analysis? Looking forward to hearing from you.

@dataspro

Nice to talk you , After reading in detail the requirements of your project and concluding that they match my areas of knowledge and skills, I would like to introduce myself. My name is Anthony Muñoz and I am the lead engineer for DS Pro IT agency. I have worked for over 10 years in Backend and software development and have successfully done multiple jobs. It will be a pleasure to work together to make your project a reality. Please feel free to contact me. I´m looking forward to working with you. I really appreciate your time and remain attentive to any request or question. Greetings

@robertb297

You listed classification, regression and clustering plus deep learning, so here is what I have actually shipped in that area rather than a skills list: a blood glucose regression model, a canine hip dysplasia detection model, a working-memory classification model, and a customer churn and lifetime value project with a recommendation engine for a client. All of it is on my Freelancer portfolio. How I work: clean and check the data first, since leakage and duplicate rows are behind most models that look great and then fall apart. Then a simple model as the reference point before anything heavier, proper cross-validation, and error analysis so you can see where it fails instead of one accuracy number. Code comes as a reproducible notebook or script with a short README. Stack is Python with pandas, NumPy, scikit-learn, matplotlib, and PyTorch or TensorFlow when deep learning is really justified. Rate is $8 per hour, around 20 hours a week available. What kind of data are you working with, and is this one project or an ongoing stream?

@AnjosGustavo

Hi, This strongly matches my Senior Data Science/ML Engineering background. I’ve built real-world ML solutions across financial, industrial, sensor, medical, NLP, and computer-vision domains using Python, pandas, NumPy, scikit-learn, PyTorch/TensorFlow, XGBoost, and statistical modeling. My approach is: data validation - EDA - feature engineering - modeling - rigorous evaluation/error analysis - optimization - explainable insights. I focus on discovering valuable patterns and making evidence-based inferences, not simply training models. Each project is treated as research: assumptions are tested, results reproduced, and code structured as an expandable pipeline for future models and AI functionality. I’m motivated by challenging problems and take strong ownership of quality, understandable delivery, and deadlines. Let’s work together and give it a try. Best, Gustavo

@dinobartolome

Your posting is basically looking for someone who lives in pandas, scikit-learn and TensorFlow daily. I want to be straight with you -- my 10+ years are primarily in full-stack web development and automation, not dedicated data science. I do work in Python regularly for scripting, data processing and building pipelines, and I've built structured data extraction systems that feed into classification workflows (one example: an NLP classifier that sorts 280k+ job postings into skill clusters from a MySQL warehouse). But the deeper ML model development and evaluation workflow you're describing -- classification, regression, clustering, deep learning architecture selection -- isn't my daily work. Where I could add real value is on the data engineering and pipeline side. Cleaning messy real-world datasets, building reproducible ETL flows, standing up the infrastructure that makes your models actually usable in production. If that's part of what you need alongside the pure data science work, I'm a good fit for that piece. If you specifically need someone whose core discipline is ML model development and statistical analysis, you'd probably want a dedicated data scientist. But if you want someone who can handle the full pipeline from raw data ingestion through to a working production system, let's talk -- I can jump on a call this week to see if there's a fit.

@abdelhafidg

Hi, I’m a Python/AI developer with hands-on experience in data analysis, machine learning, predictive modeling, feature engineering, model evaluation, and AI application development. I can help with the complete workflow from raw data to reproducible results: • Data cleaning, preprocessing and feature engineering • Exploratory Data Analysis using Pandas/NumPy • Statistical analysis and pattern discovery • Classification, regression and clustering • Model evaluation, optimization and error analysis • Clear visualizations and analytical reporting • Clean, reproducible Python code and documentation My broader AI work also includes NLP, computer vision, OCR, document understanding, and AI model development. Relevant projects include ChurnGuard, where an ML inference pipeline was optimized to approximately 2.3ms per record, and LexiQ, an AI document-analysis platform using RAG, embeddings, vector search and LLMs. I also work with Scikit-learn, XGBoost, LightGBM, Optuna, Pandas, NumPy, Matplotlib and related Python tooling. My approach is to first understand the dataset and business objective, establish a strong baseline, then iterate through preprocessing, feature engineering, model comparison and evaluation while keeping everything reproducible. I’m available for ongoing or project-based work and can adapt to the dataset and objectives you provide.

@alfatrade2

Your list covers cleaning, EDA, classification, regression, clustering and deep learning, but there's no dataset, target variable or success metric, and that decides everything. Deep learning on a small tabular set often loses to plain gradient boosting, so I'd only reach for it if the data justifies it. I built an AI pipeline on Claude and OpenAI APIs for classification and analysis. What data do you have and what should the model predict?

@indreshpnk

Hello, I understand you need an AI & Data Science professional for data cleaning, EDA, visualization, machine learning, and AI model development using real-world datasets. The goal is to turn raw data into meaningful insights and reproducible predictive solutions. Here’s what I can provide: * Clean, preprocess, and analyze datasets using Python, Pandas, NumPy, and statistical techniques. * Build and evaluate classification, regression, and clustering models with Scikit-learn. * Create clear visualizations, perform error analysis, and explain results with practical insights. I bring over 4+ years of experience in Python, data processing, machine learning, backend development, and AI-focused applications, with a strong focus on clean, reproducible, and maintainable solutions. Just to clarify a few things: * Do you already have the dataset and a defined prediction/analysis objective? * Which model type or business problem should be prioritized initially? Please come to the chat box to discuss more about your project. Best regards Indresh Kushwaha

@billybryan98

What specific datasets will we be working with, and do you have any preferred tools for data visualization? For the exploratory data analysis, have you identified key metrics or insights you wish to focus on? I have extensive experience in Python and machine learning, having successfully developed predictive models and deep learning solutions. My background in data analysis equips me to turn raw data into actionable insights. I will begin with data cleaning and preprocessing using libraries like Pandas and NumPy. Then, I'll conduct EDA to identify trends, followed by building and optimizing machine learning models. Throughout the process, I will provide clear visualizations and reports to communicate findings. Please feel free to reach out to me. I look forward to working with you. Best regards, Billy Bryan

@chamanrajragu

Hi — I'm Chaman, based in Chennai (IST), and available for the 40 hours a week you mentioned. How I'd work on each dataset: 1. Cleaning and preprocessing in pandas/NumPy: missing values, types, outliers, leakage checks. Every step goes into a reproducible notebook, never done by hand. 2. EDA with clear matplotlib/seaborn visuals and a short written summary of the patterns that actually matter. 3. Modelling in scikit-learn (classification, regression, clustering), starting with a simple baseline so every improvement is measurable. PyTorch when deep learning genuinely helps. 4. Proper evaluation: train/validation/test splits or cross-validation, the right metric for the goal, and an error analysis of where the model fails. 5. A plain-English write-up: what was done, why, what the results mean, and the caveats. Rather than point you at unrelated samples, I'd suggest a small first task on one of your real datasets. You'll see my code, process and communication directly, and decide from there. What's the first dataset and the question you want answered?

@abhilashr53

Your goal is straightforward: turn messy, real-world datasets into clean, reproducible Python pipelines, accurate ML models, and insights that actually drive decisions, not just pretty charts that sit in a folder. Here is how I work. Every engagement starts with understanding your data and business question before touching a line of code. I then move through structured EDA using Pandas, NumPy, Matplotlib, and Seaborn to surface patterns early, before building and comparing models via Scikit-learn for classical ML and TensorFlow or PyTorch when deep learning is warranted. Each model gets rigorous cross-validation, error analysis, and plain-English documentation so you can defend the results to any stakeholder. My background covers classification, regression, clustering, NLP, time series, and computer vision across domains including finance, healthcare, and e-commerce data. I write clean, commented, version-controlled code you can hand to another engineer tomorrow without confusion. Typical turnaround: EDA and baseline model within three to five days, full pipeline and report within two weeks, depending on data complexity. One risk I always address early is data leakage, which silently inflates accuracy and destroys production performance. I catch it at the preprocessing stage. What does your current dataset look like, and what decision are you ultimately trying to make with it?

@SoneJ

Hello, I understand you're seeking an AI and Data Science expert to transform raw datasets into actionable insights and predictive models. My high-level approach involves meticulous data cleaning, thorough exploratory data analysis, and developing robust machine learning models tailored to your specific needs. To ensure alignment, could you clarify the types of datasets you'll be working with? Additionally, what specific outcomes are you hoping to achieve from this analysis? At Studio SJ, we prioritize managed delivery, with me overseeing communication, scope, and quality assurance. I can source, check, or verify any required specialists for your project. Please note that we operate on paid fixed-price, milestone, or hourly engagements. Is that acceptable for your project? Looking forward to discussing this further. Sone Joubert Studio SJ

@pinkeegupta

Hello, I am an experienced specializing in AI-powered applications, AI API integration, automation, chatbots, machine learning, and custom AI solutions. I can help you build your AI project with: AI/ML model integration and customization OpenAI / Gemini / Claude and other AI API integration AI chatbot & conversational systems* Automated workflows and intelligent processing Backend APIs, database & secure architecture Web/mobile application integration AI & Frameworks: TensorFlow, PyTorch, Cursor, Claude, Gemini, nano, ChatGPT. Python Development: Pandas, NumPy, Scikit-learn, Fast API, Flask, Django Data Science & Machine Learning: Data cleaning, EDA, predictive modeling, AI/ML solutions Data Analytics: Statistical analysis, reporting, automation, data mining I focus on building scalable, secure, and production-ready AI solutions according to your exact requirements. Please share your project details, and we can discuss the scope, technology stack, timeline, and budget. Looking forward to working with you. Regards, Anju

@CharlSche

Just completed a similar project involving machine learning model development, and I can jump right into yours without delay. I’m well-versed in data cleaning and preprocessing, essential for transforming raw datasets into actionable insights. With a strong background in Python, I excel in exploratory data analysis, predictive analytics, and creating clear data visualizations using tools like Pandas and Matplotlib. My expertise extends to developing and optimizing machine learning models, including classification and deep learning techniques, ensuring that the results are both reproducible and practical for business applications. I would appreciate the chance to discuss your project in further detail. Best Regards, Charl

@GenomyR

Hi, Turning raw datasets into actionable machine learning models requires a clean pipeline that prevents silent data leakage during preprocessing and controls inference latency once deployed. Many predictive pipelines fail in production because feature engineering is done on full datasets rather than strictly inside cross-validation folds, leading to artificially inflated accuracy metrics that crumble on unseen data. I will structure your end-to-end Python pipeline using Pandas, Scikit-learn, and PyTorch. First, I will build reproducible data cleaning and exploratory data analysis scripts with explicit schema validation to handle missing values and outliers cleanly. For modeling, I will implement robust baseline algorithms (logistic regression, tree-based ensembles, or neural networks) before running targeted feature selection, hyperparameter tuning, and error analysis. If the project requires deploying an LLM or custom inference endpoint, I will optimize token usage, enforce structured JSON output schemas, and implement streaming UI handling to manage request timeouts. I can deliver the initial exploratory analysis, baseline models, and reproducible codebase within 2 business days. Which specific model provider or execution environment (such as OpenAI, Claude, or a local PyTorch stack) are you planning to deploy this pipeline on? Best regards, Tumisang