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Narola Infotech

4.7

2155

10.0

97%

Surat, India

$15 USD per hour

⭐ Top 1% on Freelancer.com | Narola Infotech - Digitalising Businesses Since 2005 ✎ About Us We are Narola Infotech, a globally recognized IT solutions company with 20+ years of experience delivering high-performance web, mobile, and enterprise software solutions to 1500+ happy clients across 50+ countries. Trusted by Fortune companies like CGI, L&T, Infosys, Biocon, and TVSNext. We don't just build software, we build lasting partnerships. Our approach is simple: understand your vision, deliver beyond expectations, and stay with you every step of the way. ✎ Why Choose Us? ➬ 20+ Years of Industry Experience ➬ 350+ Talented IT Professionals ➬ 3000+ Successfully Delivered Projects ➬ 95% Client Retention Ratio ➬ 4.9 ⭐ Clutch Rating ➬ 100% Project Success Ratio ➬ Clutch Top 1000 Companies Global 2022 ➬ Best Outsourcing Company - SGCCI ✎ Our Services ➬ Custom Web & Mobile App Development ➬ Custom Enterprise Software Development ➬ AI/ML & Data Analytics Solutions ➬ QA & Software Testing (Automation, Security, Performance) ➬ DevOps, Cloud Migration & Managed Services ➬ Salesforce Consulting, Implementation & Support ➬ Staff Augmentation - Dedicated Teams or Individual Developers ➬ Support & Maintenance ✎ Tech Stack ➬ Frontend: ReactJS, AngularJS, VueJS, NextJS, NuxtJS ➬ Backend: Node.js, Java, PHP, Laravel, .NET, Python, Django, WordPress ➬ Mobile: Flutter, React Native, iOS (Swift), Android (Java/Kotlin) ➬ Cloud & DevOps: AWS, Azure, Docker, Kubernetes, Jenkins, CI/CD, Terraform ➬ Database: MySQL, MongoDB, PostgreSQL, Firebase, MS-SQL ➬ Other: Salesforce, MS Dynamics 365, Power Platform, Big Data, AI/ML, Automation ✎ Industries We Serve ● Fintech ● Healthcare ● Retail & Ecommerce ● Education ● Real Estate ● Media & Entertainment ●Transport & Logistics ✎ Our Strengths ➬ Agile Development Methodology ➬ Daily & Weekly Progress Reports ➬ Clear Communication & Collaboration ➬ 100% Plagiarism-Free & Secure Delivery ➬ Guaranteed Data Privacy & NDA Protection ➬ Flexible Engagement Models: ● Fixed Cost ● Time & Material (Hourly) ● Dedicated Team ● Hybrid Model (Fixed + Hourly) ✎ Our Promise "You will get the value of every penny you spend. We are committed to driving your business forward with reliable, quality solutions powered by technology and innovation." - Ashish Narola, CEO

Naive Bayes Classifier PHP ASP Java JSP

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Content360

4.9

492

8.3

99%

Karachi, Pakistan

$25 USD per hour

Hello, I'm a data analyst with 8+ years of hands-on experience in transforming raw data into actionable insights. I have been assisting businesses, researchers, and doctoral scholars globally in effectively analyzing qualitative and quantitative data using tools such as Stata, SPSS, R, Python, EViews, Power BI, and Excel. In addition to my core data analysis expertise, I have expanded my capabilities in AI automation, machine learning, and large language model (LLM) applications. I work with frameworks like LangChain and Retrieval-Augmented Generation (RAG) to develop intelligent solutions such as chatbots, knowledge retrieval systems, and automated analytics pipelines — enabling organizations to streamline workflows and enhance decision-making with AI-powered insights. With a blend of domain expertise in business and science and robust statistical skills, I am equipped to decode complex datasets, unveil hidden patterns and trends, and present findings as actionable predictive and explanatory models. ► My Services: ✔️ Statistical Analysis using R / SPSS / STATA / EViews: -Descriptive Statistics: Means, Medians, Modes, Standard Deviations, Frequency Distributions, and Cross-tabulations -Hypothesis Testing: T-Tests, Chi-square Tests, and ANOVA (ANOVA/ANCOVA/MANOVA) -Regression Analysis: Linear and Logistic Regression Models -Time Series Analysis: Trend Analysis, Seasonality, and Forecasting -Multivariate Analysis: PCA, Factor Analysis, and Cluster Analysis -Econometric Analysis: VAR, VECM, and Panel Data Analysis ✔️ Machine Learning & AI Automation: -Supervised and Unsupervised Learning (Regression, Classification, Clustering) -Natural Language Processing (NLP) and LLM Applications -LangChain and RAG-based Chatbots, Assistants, and Knowledge Systems -Automated Data Pipelines and Predictive Modeling ✔️ Power BI Reporting: -Extracting data from various sources (APIs, Excel files, SQL Servers, etc.) -Cleaning and transforming data using Power Query on Datasets or Dataflows -Establishing data model relationships -Creating and validating measures using DAX -Developing and testing charts and filters -Designing user-friendly interfaces ✔️ Data Visualization: -Histograms, Scatter Plots, Box Plots, Heatmaps, and Dendrograms ✔️ Reporting and Documentation: -Comprehensive statistical reporting with clear visualizations and actionable insights tailored to your needs — ensuring that findings are both understandable and practical for decision-making. With this broad technical expertise, I provide free consultations and strategic recommendations to help clients address real-world challenges and drive smarter, data-informed decisions in today’s AI-driven world. Best Regards, Aneesa

Naive Bayes Classifier Python Visual Basic Copywriting Legal

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Muhammad A.

5.0

94%

Gojra, Pakistan

$20 USD per hour

Hi, I’m Muhammad Ahmad 👋 I’m an AI Engineer and Data Scientist focused on building production-ready AI systems that solve real business problems — from predictive analytics to LLM-powered applications and workflow automation. I specialize in: — Customer churn & behavioral prediction — AI automation systems — RAG-based LLM applications — Scalable ML pipelines and analytics workflows I help businesses turn raw data into operational decisions through reliable, deployable AI solutions — not just experimental models. What I bring: — End-to-end ownership (data pipelines → modeling → deployment → monitoring) — Strong production experience with telecom and large-scale datasets — Focus on scalability, performance, and measurable business impact Core expertise: • Machine Learning: XGBoost, Random Forest, classification, forecasting • Generative AI: RAG pipelines, embeddings, FAISS, LLM fine-tuning • Backend & Deployment: FastAPI, Docker, AWS, Linux • Data & Analytics: SQL, ETL pipelines, automation, reporting systems Selected impact: — Built production churn & dormancy prediction systems supporting retention strategies — Automated reporting workflows saving 10+ hours weekly — Developed AI-powered RAG chatbots for intelligent document retrieval — Delivered 30+ successful projects across multiple industries If you need someone who can design, build, and deploy reliable AI systems that integrate into real business operations, let’s connect.

Naive Bayes Classifier C Programming JavaScript Python Linux

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Alexander B.

4.9

5.2

100%

South Hobart, Australia

$80 USD per hour

I currently work as a business analyst, and have a passion for Excel. I can produce very complicated formulas and write VBA code to perform anything you need, including web data scraping. I have great communication on projects, and will be able to offer not only great quality output, but recommendations on things to improve the project too!

Naive Bayes Classifier PHP JavaScript XML Python

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Zain F.

5.0

2.5

80%

Lahore, Pakistan

$10 USD per hour

Welcome to my profile! I am a highly skilled and passionate Data Scientist with extensive experience in Machine Learning, Deep Learning, NLP, Computer Vision, Data Analysis, and Generative AI. I specialize in building intelligent systems, developing advanced algorithms, and creating cutting-edge AI solutions that drive business growth and innovation. What I Bring to the Table: 1) Machine Learning & Deep Learning: Proficient in designing, implementing, and optimizing ML/DL models for predictive analytics, pattern recognition, and decision-making using TensorFlow, PyTorch, and Scikit-learn. 2) Natural Language Processing (NLP): Expertise in text mining, sentiment analysis, chatbots, and language modeling using Transformer models like BERT, GPT, and T5. 3) Computer Vision: Skilled in object detection, image classification, and segmentation using architectures like YOLO, Faster R-CNN, and Vision Transformers. 4) Data Analysis & Visualization: Experienced in data wrangling, EDA, and visualization using Pandas, NumPy, Matplotlib, and Seaborn to transform raw data into actionable insights. 5) Generative AI: Expert in text generation, image synthesis, and AI-powered content creation using GANs, VAEs, and Transformers. 6) Chatbot Development: Proven track record in designing AI-powered chatbots using LLMs like GPT, Llama, and Mistral for enhanced customer engagement. Why Choose Me? ✅ Proven expertise in delivering AI/ML projects across healthcare, finance, e-commerce, and security. ✅ Innovative problem solver focused on optimizing processes and driving automation. ✅ Client-centric approach with transparent communication and collaborative strategies. ✅ Stay updated with cutting-edge AI technologies to deliver state-of-the-art solutions. Let’s build the future together! Whether you need AI models, data analysis, chatbots, or computer vision solutions, I’m here to help you achieve your goals. Feel free to reach out to discuss your project!

Naive Bayes Classifier Python Data Processing Machine Learning (ML) MySQL

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Umair I.

5.0

2.2

67%

Jampur, Pakistan

$15 USD per hour

I'm an AI/ML Engineer with 3+ years of experience building production-grade systems across NLP, Computer Vision, and backend API Development. I specialize in the full ML lifecycle from data preprocessing and model training to real-time deployment and bring the same rigor to research as I do to client work. In Computer Vision, my expertise spans object detection and tracking (YOLO, Faster R-CNN, DeepSORT), segmentation (SAM, Mask R-CNN, U-Net), facial analysis (RetinaFace, MediaPipe, InsightFace), pose estimation (DCPose, OpenPose, HRNet), OCR (Tesseract, EasyOCR, PaddleOCR), and generative models (Stable Diffusion, CycleGAN, Pix2Pix). I've shipped real-world CV systems including a shoplifting detection pipeline using YOLOv8 on live CCTV feeds, a sports pose and equipment tracker for tennis motion analytics, an AR face filter system using MediaPipe landmarks, a license plate and car make recognizer for traffic automation, and a VisionAssist Android app that announces nearby objects with distance for visually impaired users. My satellite object detection model outperformed published SOTA by 3%. On the NLP and LLM side, I've fine-tuned LLaMA 2, Mistral-7B, and RoBERTa using QLoRA for tasks like mental illness detection, legal document intelligence, and AI-generated text classification (98.4% accuracy). I've built full RAG pipelines with BGE-M3 + BM25 hybrid retrieval backed by Qdrant, and developed DeepContext — a PyPI-published hierarchical agent memory system with 179 passing tests. For backend systems, I build async FastAPI services with WebSocket support, real-time financial signal pipelines using FinBERT, and Polymarket-integrated data architectures. I deploy models using TensorRT, ONNX, and TFLite across cloud and edge platforms including AWS, Azure, and NVIDIA Jetson. I hold certifications from Andrew Ng's ML and Deep Learning Specializations, a DataCamp Data Scientist track, and an MLOps Bootcamp. I build systems that work reliably in production and I take full ownership from first commit to final deployment.

Naive Bayes Classifier Python Photography Data Processing Android

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Muhammad Kashif K.

5.0

1.3

50%

Kamar Mushani, Pakistan

$10 USD per hour

I am a Python-based AI Engineer specializing in Agentic AI systems and Retrieval-Augmented Generation (RAG) applications, helping businesses build intelligent, automated, and scalable AI solutions. Core Expertise: Agentic AI systems – autonomous agents, tool usage, multi-step reasoning, workflow automation RAG applications – document ingestion, embeddings, vector databases, semantic search LLM integration – chatbots, enterprise assistants, prompt engineering Machine Learning & Data Science – supervised/unsupervised learning, model evaluation Genetic Algorithms & optimization techniques for complex problem solving Technical Skills: Python (advanced), Object-Oriented Programming, software architecture Web scraping & data extraction (structured and unstructured data) Database management – SQL & NoSQL, vector databases API development & integration Scalable, production-ready AI systems Professional Experience: 2+ years in the networking domain, delivering large-scale (mega) projects Strong background in system design, performance optimization, and data pipelines Experience building robust, distributed AI applications What I can deliver: Custom Agentic AI solutions End-to-end RAG systems for private knowledge bases AI-powered chatbots and assistants Intelligent automation using Python & LLMs Reliable, maintainable, and well-documented solutions If you’re looking for an AI professional who combines cutting-edge AI with real-world engineering experience, I’m ready to help.

Naive Bayes Classifier Python Algorithm Web Scraping Machine Learning (ML)

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Ziad S.

5.0

1.4

100%

Cairo, Egypt

$700 USD per hour

I have worked on text classification, data labeling, and AI model development. I am highly detail-oriented, fast, and reliable in completing microtasks with high accuracy. I am passionate about AI training, content evaluation, and improving data quality for machine learning systems.

Naive Bayes Classifier AI Text-to-text Sentiment Analysis Natural Language Generation AI Chatbot Development

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Wisal M.

5.0

1.4

100%

Cairo, Egypt

$10 USD per hour

I am a Data & AI specialist with hands-on experience in Machine Learning, Data Analysis, and Python development. I work on transforming complex data into clear insights and practical, real-world solutions. My expertise includes data preprocessing, feature engineering, building and optimizing machine learning models, and performing exploratory data analysis (EDA) to uncover meaningful patterns. I write clean, well-structured, and scalable Python code to ensure solutions are efficient and maintainable. I focus on delivering reliable results, clear communication, and solutions that are not only technically strong but also easy to use and applicable in real scenarios.

Naive Bayes Classifier Python Data Entry Excel UML Design

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How to hire a great freelance Naive Bayes Classifier Developer

A Naive Bayes Classifier Developer is a machine learning specialist who builds probabilistic classification models using Bayes' theorem to predict categories from labeled data such as text, emails, sensor readings, or transactional records. These developers translate raw datasets into production-ready classifiers that power spam filtering, sentiment analysis, document categorization, medical diagnosis support, and fraud detection systems. By combining statistical modeling, feature engineering, and software engineering skills, a Naive Bayes developer delivers fast, interpretable, and surprisingly accurate models that often outperform heavier algorithms on text-heavy or high-dimensional problems.

What a Naive Bayes Classifier developer delivers

A freelance Naive Bayes specialist takes your raw data and produces a trained, validated, and deployable classification model. They handle the full pipeline: data cleaning, feature extraction, model training, hyperparameter tuning, evaluation, and integration into your application or workflow.

Typical deliverables include:

Trained Gaussian, Multinomial, Bernoulli, or Complement Naive Bayes models matched to your data type

Cleaned and preprocessed datasets with documented feature engineering steps

Text vectorization pipelines using TF-IDF, count vectorization, or word embeddings

Cross-validation reports with precision, recall, F1-score, ROC-AUC, and confusion matrices

Model serialization in pickle, joblib, or ONNX formats for production use

REST APIs built with Flask, FastAPI, or Django for real-time inference

Jupyter notebooks documenting exploratory data analysis and model selection rationale

Comparative benchmarks against logistic regression, SVM, or gradient boosting baselines

Core tasks and technical scope

A Naive Bayes Classifier Developer handles the statistical assumptions, smoothing techniques, and feature independence considerations that make these models work well in practice. Expect work across data preparation, model development, and deployment.

Implementing Multinomial Naive Bayes for text classification tasks like spam detection and topic labeling

Applying Laplace or Lidstone smoothing to handle zero-frequency problems

Building Gaussian Naive Bayes models for continuous numerical features

Performing feature selection using chi-square tests, mutual information, or variance thresholds

Handling class imbalance through resampling, class weighting, or Complement Naive Bayes

Tuning alpha smoothing parameters and prior probabilities via grid search or Bayesian optimization

Comparing model performance against ensemble methods and deep learning baselines

Deploying models to AWS SageMaker, Google Cloud AI Platform, or Azure Machine Learning

Tools, libraries, and frameworks

Most Naive Bayes work happens in Python, though R remains common in research and statistical consulting contexts. A capable developer is fluent across the standard machine learning toolchain.

Python libraries: scikit-learn, NLTK, spaCy, Gensim, pandas, NumPy

Text processing: TF-IDF vectorizers, CountVectorizer, regular expressions, tokenization pipelines

R packages: e1071, klaR, naivebayes, caret

Visualization: Matplotlib, Seaborn, Plotly for performance curves and feature importance

Deployment: Flask, FastAPI, Docker, Kubernetes, MLflow for experiment tracking

Big data: Apache Spark MLlib for distributed Naive Bayes training on large corpora

Notebooks and IDEs: Jupyter, Google Colab, PyCharm, VS Code

Industries and common use cases

Naive Bayes classifiers remain a workhorse algorithm in any domain where speed, interpretability, and reasonable accuracy matter more than squeezing the last percentage point of performance.

Email and messaging: spam filtering, phishing detection, intent classification

E-commerce and retail: product categorization, review sentiment analysis, recommendation filtering

Healthcare: diagnostic support systems, patient risk stratification, clinical text classification

Finance: fraud detection, credit scoring, transaction categorization

Media and publishing: news topic tagging, content moderation, document routing

Marketing: lead scoring, customer segmentation, social media sentiment tracking

Legal and compliance: document classification, contract clause identification

How to evaluate Naive Bayes developer candidates

Strong candidates combine statistical literacy with practical machine learning engineering. Look for portfolios showing end-to-end projects with documented evaluation metrics, not just trained models in isolation.

Key signals to assess:

Demonstrated understanding of when Naive Bayes is the right choice versus alternatives

Published notebooks, GitHub repositories, or Kaggle competition entries involving classification tasks

Experience with the specific Naive Bayes variant your data requires (Multinomial, Gaussian, Bernoulli, Complement)

Fluency with scikit-learn pipelines and proper train-test-validation splits

Awareness of the conditional independence assumption and how to handle violations

Background in NLP, statistics, applied mathematics, or computer science

Sample interview questions you can use directly:

Can you explain why Naive Bayes is called "naive" and when that assumption breaks down in real datasets?

Which Naive Bayes variant would you choose for a binary text classification problem with TF-IDF features, and why?

How would you handle a feature that appears in the test set but never in the training data?

Walk me through how you would evaluate a Naive Bayes model on an imbalanced dataset with 95 percent negative examples.

Can you describe a project where you compared Naive Bayes against another algorithm and explain how you chose between them?

Why hire Naive Bayes Classifier developers on Freelancer.com

Freelancer.com gives you access to a global community of machine learning engineers, data scientists, and NLP specialists with verified profiles, public ratings, and portfolios you can review before awarding work. Whether you need a quick prototype for a text classification task or a production-grade fraud detection pipeline, freelancers on Freelancer.com cover every level of experience and specialization. Clients set their own budgets, receive competitive bids, and benefit from Milestone Payments that release funds only when work meets expectations. The scale of the marketplace means you can shortlist qualified candidates quickly, regardless of time zone or project complexity.

Post a project on Freelancer.com today and connect with skilled Naive Bayes Classifier developers ready to build, train, and deploy the classification model your project needs.

What are the steps to hiring a freelance Naive Bayes Classifier developer?

Hiring the right Naive Bayes specialist starts with a clear brief that describes your data, your classification objective, and the environment where the model will run. The process below walks you through publishing your project, reviewing proposals, and selecting a developer whose track record matches your technical needs.

Step 1: Post your project on Freelancer.com

Your project brief is the single biggest factor in the quality of bids you receive. A specific, technical brief filters out generalists and attracts developers who genuinely understand probabilistic classification. Head to the post project page , where you fill in the brief, scope, timeline, and budget before publishing the project for freelancers to bid on.

Describe your dataset: size, format (CSV, JSON, database), number of classes, and labeling status

State the classification objective clearly (spam detection, sentiment analysis, document categorization, etc.)

Specify the Naive Bayes variant if known, or ask the freelancer to recommend one

Note required deliverables such as a trained model, evaluation report, REST API, or Jupyter notebook

List technical constraints: preferred language (Python or R), deployment target, latency requirements, accuracy thresholds

Indicate your timeline and budget range to attract proposals aligned with your scope

Step 2: Review bids from freelancers

Bids are short proposals that reveal how each freelancer interprets your brief and approaches the problem. Read them carefully rather than sorting purely by price. A strong Naive Bayes proposal demonstrates understanding of your data type, suggests an appropriate model variant, and proposes a realistic evaluation strategy.

Look for proposals that name the specific Naive Bayes variant suited to your data and justify the choice

Check whether the freelancer asks clarifying questions about class balance, feature types, or labeling quality

Assess whether the proposed timeline includes time for exploratory data analysis, model tuning, and validation

Look for mentions of relevant tools such as scikit-learn, NLTK, pandas, or specific vectorization techniques

Review any sample work attached, especially classification notebooks or GitHub repositories

Use Freelancer.com's chat to ask shortlisted candidates how they would handle edge cases in your dataset

Step 3: Evaluate freelancers and award the project

The final decision combines proposal quality with profile evidence. Review portfolios for consistency across multiple classification projects rather than relying on a single impressive example. Past client reviews often reveal communication style, adherence to deadlines, and willingness to iterate on model performance.

Examine portfolio depth: look for multiple machine learning or NLP projects with documented results

Check star ratings and the total number of completed projects on the freelancer's profile

Read written client reviews, paying attention to comments about technical accuracy and communication

Confirm verified payment method and review the freelancer's completion and on-time delivery rates

Look for certifications, academic credentials, or published work in data science or statistics

Award the project and use Freelancer.com's Milestone Payments to release funds as deliverables such as cleaned data, trained models, and evaluation reports are completed

How long does a Naive Bayes classifier project take?

A focused classification task on clean, labeled data can be completed in a few days, including training, evaluation, and basic deployment. Larger projects involving messy data, custom feature engineering, or integration with production systems typically run two to six weeks depending on dataset size and accuracy requirements.

What is the difference between a Naive Bayes developer and a general machine learning engineer?

A general machine learning engineer works across many algorithms, while a Naive Bayes specialist has deep expertise in probabilistic classification, text vectorization, and the statistical assumptions specific to Bayesian methods. For text-heavy or interpretability-focused projects, hiring a specialist often produces faster, cleaner results than a generalist.

Can I hire a Naive Bayes freelancer for a one-off classification task?

Yes. Many clients post short projects such as building a spam filter, categorizing a product catalog, or analyzing sentiment in a review dataset. Freelancers on Freelancer.com regularly handle these one-off engagements with fixed-price or hourly arrangements.

Do I need a Naive Bayes developer or a deep learning specialist?

If your dataset is small to medium, your features are largely text or categorical, and interpretability matters, Naive Bayes is often the better choice. Deep learning becomes worthwhile when you have very large datasets, complex feature interactions, or need state-of-the-art accuracy on tasks like image recognition or language generation.

What data do I need to provide to get started?

At minimum, you need a labeled dataset where each example is assigned to a class. The freelancer will also need a description of your target outcome, accuracy expectations, and any constraints around deployment environment, latency, or interpretability.