Python Email Classifier Automation

@AZTURK1

Hi there, I understand you need a compact Python service that classifies incoming Gmail messages into Work, Promotion, or Personal and automates move/flag/reply actions, I'm confident I can deliver a reliable, retrainable solution that runs headless on a small Linux VPS. - Deliver a Python IMAP listener (cron-friendly or always-on) with classification + action hooks - Provide configurable JSON file for folders, thresholds, templates and retraining data import - Include unit-tested sample dataset, README, inline comments and a console report with precision/recall - Ensure safe move/flag/reply logic with no duplication and graceful error handling Skills: ✅ Machine Learning Algorithms ✅ Python ✅ retraining-from-JSON/CSV workflow ✅ IMAP integration (Gmail) & Linux VPS deployment ✅ reliability, error handling and no-message-loss guarantees Certificates: ✅ Microsoft® Certified: MCSA | MCSE | MCT ✅ cPanel® & WHM Certified CWSA-2 I can start immediately and deliver a working prototype within the first day; Do you prefer a lightweight rules+scikit-learn pipeline or a fine-tuned transformer for best accuracy given limited training data, and can you share a small sample export (30 per category) for initial validation? Best regards,

@techplusintl

Hi there, I can start immediately to build your Python solution for email classification. I will create a script that sorts incoming messages into Work, Promotion, and Personal folders, with the appropriate actions for each type. I've previously developed a similar email automation tool that achieved over 90% accuracy. Could you clarify if you have specific templates for the auto-replies? Regards, Burhan Ahmad

@Tanmoy236

Warm greetings! I specialize in Python automation and NLP-driven email workflows, and with 9+ years of experience in Python, Data Science, and Automation, I can deliver a reliable inbox classifier that sorts, flags, and auto-replies to Work, Promotion, and Personal emails with high accuracy. Here’s how I can help: * Build a lightweight, retrainable classifier using scikit-learn/spaCy or fine-tuned transformers * Implement IMAP integration to move emails, flag, and send templated replies as configured * Provide a clean configuration file for folders, thresholds, and reply templates * Include inline comments, README with setup/testing instructions, and validation logs showing precision/recall Do you want the system to run continuously as a listener or triggered via cron job for batch checks?

@koolvishruth

Hi, I have 8 years of experience in Python,Machine Learning engineering.I have worked with 116+ clients. Let’s connect

@HiraMahmood4072

Hello, I have over 7 years of experience in Data Processing, Data Science, Machine Learning Algorithms, and Python. I have carefully read the requirements for the Python Email Classifier Automation project. To address the project needs, I propose developing a Python script that utilizes a machine-learning model or rules-based system to classify incoming emails as Work-related, Promotions, or Personal. The script will interact with the IMAP protocol on a Gmail account, automatically organizing emails into respective folders, flagging for follow-up, and sending appropriate acknowledgments based on the classification. Key deliverables will include well-commented Python source code, a configurable setup file, a README for easy setup and testing, and a report showcasing precision/recall metrics on a sample email dataset. I am confident in my ability to deliver a solution that meets the specified acceptance criteria and ensures high accuracy in email classification. Let's discuss further details in the chat. You can visit my Profile: https://www.freelancer.com/u/HiraMahmood4072 Thank you.

@brayanstiven5

Hi, there, I am an experienced freelancer skilled in Software Architecture, Python, Natural Language Processing, and Automation. With expertise in developing machine learning algorithms and data processing solutions, I am well-equipped to tackle the project of creating a Python Email Classifier Automation system for you. ✅ Leveraging tools like scikit-learn and spaCy, I will build a lightweight classifier to categorize incoming emails into Work-related, Promotion, or Personal categories. ✅ The solution will include a flexible configuration file for easy customization of folder names, thresholds, and reply templates without code modification. ✅ Implementing an IMAP connection through Gmail on a Linux VPS, the system will automate the sorting, flagging, and acknowledgment of emails based on the classification. ✅ Training the model to ensure at least 90% accuracy across the categories and providing a concise report showcasing precision/recall metrics on a sample dataset. ✅ Deliverables will consist of well-documented Python source code, a comprehensive README for setup and testing, and error-handling mechanisms for connectivity issues. I look forward to working with you. Best Regards, Brayan

@GameOfWords

As an AI-powered solutions developer, I am confident in my ability to construct an automated Python system that will revolutionize your email management. Over the years, I have sharpened my skills in automation, which pairs perfectly with this project's requirement to categorize and process emails seamlessly. Drawing from my experience devising smart automation pipelines and working with tools like n8n and Zapier, I have a deep understanding of how vitally workflows are executed and understand that no message loss or duplication should occur during the entire process. Moreover, my solid command of Python is complemented by my extensive background in data analysis and software architectures-making me adept at developing machine-learning models using libraries like scikit-learn or spaCy, all of which you have indicated as welcome options for this project. I can harness these libraries to build a lightweight yet accurate classifier and ensure the system's easy retraining or keyword expansion, as per your request. By choosing me for this project, you will have more than just lines of code; you'll have a reliable partner committed to delivering quality work that meets stringent acceptance criteria.

@maximg10

⭐Hello, I’m ready to assist you right away!⭐ I believe I’d be a great fit for your project since I have extensive experience in data processing, automation, and machine learning. My expertise in Python, natural language processing, and Linux align perfectly with the requirements of your Python Email Classifier Automation project. I have successfully implemented similar solutions in the past, ensuring high accuracy and efficient automation. I have a proven track record of developing sophisticated email automation systems that streamline inbox management. With a tailored lightweight machine-learning model or rules-based approach, I will ensure precise classification of work-related, promotional, and personal emails, along with the appropriate actions for each category. My solutions prioritize accuracy and efficiency while allowing for easy retraining and customization. This project will address your critical needs by automating email classification, inbox organization, and response handling, optimizing your workflow and saving you valuable time. By leveraging tools like scikit-learn and spaCy, I will create a robust solution that meets your expectations and delivers reliable performance. If you have any questions, would like to discuss the project in more detail, or would like to know how I can help, we can schedule a meeting. Thank you. Maxim

@mattafareed4848

Hello I can deliver a compact Python solution that classifies incoming Gmail messages into Work, Promotion, or Personal with ≥90% accuracy, moves and flags them appropriately, sends optional templated replies, and allows easy retraining via JSON/CSV. The script will run headless on Linux via IMAP, include a configurable file for folders and templates, inline comments, README, and a validation log of precision/recall on sample emails. Regards Muhammad

@emilesd617

Hi, I am a full-stack AI developer with 8 years of experience in software development. I am familiar with Python, IMAP email automation, NLP, scikit-learn, spaCy, machine learning, and Linux server environments. For this project, I can build a Python service that monitors your Gmail via IMAP, classifies emails using a lightweight ML or rules-based model, moves them to the correct folders, and handles flags and auto-replies with configurable settings. I'm an individual freelancer and can work on any time zone you want. Please contact me with the best time for you to have a quick chat. Looking forward to discussing more details. Thanks. Emile.

@muneeb6767

I can build a lightweight Python IMAP automation script that classifies emails (Work, Promotion, Personal) with 90%+ accuracy, moves/flags them correctly, and sends configurable auto-replies without message loss. The solution will include a retrainable ML/rule-based classifier, editable config file, VPS-ready setup, validation report (precision/recall), and clean error handling.

@xuezhe315

Hello, Building a lightweight, efficient, and precise Python Email Classifier Automation solution that meets your specific requirements and expectations is precisely what I excel in. My proficiency in Python, data processing, and natural language processing (NLP) enables me to create smart classifiers that can handle varied data patterns with high accuracy. While scikit-learn or spaCy are viable options for this project, I am also well-versed in fine-tuned transformers and other machine learning tools that might enhance the classifier's performance. To ensure a seamless experience for you, I propose a well-documented Python source code accompanied by a configuration file, empowering you to comfortably customize folder names, thresholds, and reply templates without the need for code modification. Additionally, my codebase will allow for easy retraining or keyword expansion from external files using JSON or CSV format to assist you in handling limited training data. With my extensive experience as a well-rounded software engineer who's skilled at creating intelligent systems at scale where every component speaks to each other effectively, I am certain I can deliver the benchmarked solution you seek for your inbox management automation needs. Regards.

@BlueLi0n

I have built similar email automations using Python's imap-tools and scikit-learn. To hit your 90% accuracy goal with limited data, I will use a hybrid system: strict rules for known senders and a Naive Bayes model for the rest. I can also handle the Gmail OAuth2 setup required for 2026. I am ready to start—do you have the training data ready, or should I help extract it from your current inbox history?

@RadoslavPompura

As an experienced developer who has worked extensively with Python and automation systems, I am confident that I am the perfect fit for your Python Email Classifier Automation project. Over the past years, I have developed numerous intricate automation systems and specifically in the NLP space. One notable example is an automated document classification tool which has been employed for categorizing legal documents. This required closely monitoring incoming emails from various sources and subsequently deciding which category they belonged to. Additionally, my proficiency in working with several important tools for text classification including spaCy and scikit-learn aligns perfectly with your project requirements. Having handled limited training data in the past, I understand and value the importance of simplicity when it comes to retraining or expanding keywords using JSON or CSV files. Moreover, my dedication is unmatched as I willingly devote 10-15 hours every day to my freelance work. This means that once we team up, I will be able to prioritize your project effectively and get it up and running promptly. My previous works speak for themselves as they emulate similar functions of monitoring platforms, scraping targeted contents and taking responsive actions. With all my skills in automated betting systems, odds scraping, AI NLP tools and more, reaching your minimum 90% accuracy threshold across all three categories will never be a challenge we can't conquer!

@albertc1996

Hi, From your description this is two problems: a classifier with limited training data, and a safe IMAP workflow that never double-moves or double-replies. I’d build it as a headless Python service with a config file controlling labels/folders, thresholds, keyword rules, and reply templates. For classification, a TF-IDF + linear model (scikit-learn) is a strong baseline, with rule overrides from JSON/CSV so you can tweak behavior without retraining. On the IMAP side, I’d make the pipeline idempotent using Message-ID tracking (local SQLite) so reconnects don’t cause duplicate actions, and handle Gmail’s “folders as labels” behavior carefully to avoid leaving copies behind. Cron polling is the simplest reliable option; IMAP IDLE can be added if you truly need real-time. Two quick checks: do you prefer cron polling or always-on IMAP IDLE for v1, and will you provide ~30 labeled emails per class (or can we bootstrap labels from existing Gmail labels)? If those are clear, an MVP is typically 5–7 days including precision/recall reporting and a one-command test run.

@devanshkhandekar

Hi,I’m an Applied NLP Engineer with more than 6 years of experience and I’ve built lightweight text classification + automation pipelines (intent routing, triage, spam/promo detection) that run reliably in headless Linux environments with reproducible evaluation. How I’d build your system (accuracy + “no message loss” first) *IMAP ingestion: secure IMAP listener (IDLE) or cron-based polling. Fetch only new/unseen mail, store message IDs, and keep an audit log to prevent duplicates. *Classification: start with a strong, low-data baseline (TF-IDF + Linear SVM/LogReg in scikit-learn)+ optional rule boosts (keywords/domains). If needed, upgrade to a small transformer (SBert) with calibration. Output label + confidence. *Config-driven behavior: JSON/YAML for folder names, thresholds, keyword lists, and reply templates. Easy retraining by appending labeled examples from CSV. *Actions with safety: move to folder via IMAP, set flags, and send replies through SMTP (Gmail). Use idempotent steps + rollback-safe ordering (label/move only after successful write; reply only after move succeeds). *Evaluation: validate on ≥30 emails/class, report precision/recall/F1 and confusion matrix, and tune thresholds for ≥90% accuracy. Relevant experience * Built email/helpdesk triage classifiers with confidence thresholds + human-review fallback. * Implemented reliable headless automations (retries, backoff, credential/connection error handling) with full audit trails.

@KTECH2411

I can build a compact, headless Python IMAP (Gmail) automation that classifies incoming emails as Work/Promotion/Personal and then reliably moves, flags, and auto-replies per your rules with zero message loss. The classifier will be lightweight (rules + ML option) with easy retraining/keyword updates from JSON/CSV, plus a config file for folders, thresholds, and reply templates—no code edits needed. You’ll get clean, commented source, README with one-command testing, robust error handling for auth/connectivity, and a validation report with precision/recall on 30+ samples per class aiming for 90%+ accuracy.

@chiragpipal

With a goal to streamline your inbox management effortlessly, I will develop a compact Python solution leveraging machine learning or rules-based models for accuracy. Drawing on 5 years of experience and past similar projects, I will ensure seamless integration with IMAP (Gmail) on a Linux VPS. Your inbox will be organized into Work, Promotion, and Personal folders with auto-flagging and replies, enhancing efficiency and organization. The solution's agility for retraining and expandability through JSON or CSV files will ensure future scalability. A detailed README and precision/recall validation reports will guarantee the system's reliability and performance. Let's discuss how this solution can add value to your daily workflow. Free advice on optimizing your email management awaits. Chirag Pipal Regards

@billybryan98

Hello, I hope you are doing well. I design compact, robust automation solutions and I understand you need a real-time, self-contained Python system that classifies emails into Work, Promotion, and Personal, then performs the required actions without data loss. The goal is a lightweight, headless service that runs on Linux, with an easy retraining path via JSON/CSV to grow the rules or model as your inbox evolves. I’ve built email automation and NLP pipelines using Python, scikit-learn, spaCy, and lightweight heuristics. I’ll implement a small classifier (ML or rules-based) that you can retrain from a JSON/CSV file, integrate IMAP access to Gmail, and provide a cross-platform script that can be triggered by cron or kept always-on. The code will include clear inline comments, a separate configuration file for folder names, thresholds, and reply templates, and a README with setup and a one-command test against sample messages. I’ll also produce a concise console log showing precision/recall on a test set. I can deliver a clean, reliable workflow that moves, flags, and replies as specified, with robust error reporting and clean exit behavior. Best regards, Billy Bryan