End-to-End Email Spam Classifier

@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

@hoangnn90

✅Full Experience in Mail Spam Classification and Dashboard with Python Programming(Streamlit/FastAPI)✅. ✳️I am very confident that complete your project perfectly. ✳️I can guarantee the quality of the job and deliver the result on time. I hope we will discuss in more detail via chat. Best regards!

@DeveloperStation

Hi, We’ve built similar email classification systems using Python libraries like Scikit-learn and FastAPI, and we’ve also integrated Streamlit dashboards for real-time predictions. In one project, we developed a multi-class email classifier that achieved over 90% accuracy with a custom-built model, and we later added a Streamlit dashboard for user convenience. For your project, we’ll use advanced NLP techniques like BERT embeddings and fine-tune pre-trained models to ensure high accuracy. We’ll also implement a CI/CD pipeline for automated testing and model retraining with new data. Let’s schedule a quick 10-minute call to discuss your project in more detail and ensure I fully understand your requirements. I’m eager to learn more about your exciting project. Best regards, Adil

@noursherif030

As a seasoned software engineer with a strong background in network security and data science, I am confident that I can deliver on your requirements for the end-to-end email spam classifier. My expertise lies primarily in Python, which is widely used for ML tasks, and have a deep understanding of the required principles such as text preprocessing using TF-IDF vectors. Over the course of my five-year career, I have worked on numerous projects that involved creating full production-ready pipelines. Whether it was developing IoT systems, automating API integrations, or undertaking cybersecurity-related tasks, my focus has always remained on delivering clean, robust code and ensuring seamless hand-off. My track record of utilizing automation scripts to improve efficiency and reduce errors will be an asset in this project. In addition to meeting all of your acceptance criteria, I am happy to propose adding a small Streamlit dashboard that lets you paste in an email and view the prediction in real-time. With my comprehensive skills in full-stack web development and API integrations, incorporating this feature should pose no challenge. Let's chat further about your needs so we can get started right away!

@juanjosec63

Hi, I have strong experience in Python, machine learning pipelines, NLP (text classification), FastAPI deployment, and end-to-end model packaging including training, evaluation, and production-ready API services. I can build your email spam classifier pipeline with clean TF-IDF preprocessing, model training across Logistic Regression, Naïve Bayes, and SVM with automatic model selection, plus a reproducible evaluation setup (accuracy, F1, ROC-AUC, confusion matrix). I also have hands-on experience deploying ML models using FastAPI with structured /predict endpoints and organizing repositories for smooth re-training and handoff. Best regards, Juan

@kamran2012

Hello there, I will deliver the full spam classifier pipeline — TF-IDF preprocessing, model training across Logistic Regression, Multinomial Naïve Bayes, and linear SVM with automatic best-model selection, a FastAPI `/predict` endpoint returning class and probability, and a metrics report with accuracy, F1, ROC-AUC, and confusion matrix. For the Streamlit dashboard, I will wire it directly to the saved model pickle rather than calling the API — this keeps the demo lightweight and eliminates a network dependency during local testing. Questions: 1) How large is the CSV roughly — thousands or hundreds of thousands of rows? This affects whether I add stratified k-fold or a simple train/test split. 2) Are the labels balanced, or is there significant class imbalance that would require SMOTE or adjusted class weights? Ready to start whenever you are. Kamran

@sf264

Hello, I will develop a full production ready pipeline from the single file that you will hand me. There will be a small Streamlit dashboard as well that will let you paste an email and see the prediction in real time. I have some questions around the email domain data so let's talk via chat and discuss more. I am looking forward to your response, Fahad.

@toriquldev123

Hello Dear! Greetings from Toriqul Global Solutions! We are a reliable and experienced web design & development company led by Engineer Toriqul Islam (B.Sc. in CSE, RUET), with over 10 years of proven industry experience delivering quality digital solutions. At Toriqul Global Solutions, we build modern, user-friendly, high-performance websites focused on simplicity, elegance, and functionality to boost engagement and business growth. I have some questions——— 1. What language (Python) and approximately how many labeled emails? 2. Do you require streaming/persistent model retraining or one-time training? Technologies We Use: Custom Websites Development Using ======>Full Stack Development. 1. HTML5 2. CSS3 3. Bootstrap4 4. jQuery 5. JavaScript 6. AngularJS 7. React JS 8. Node.js 9. WordPress 10. PHP 11. Ruby on Rails 12. MYSQL 13. Laravel 14. .NET 15. CodeIgniter 16. React Native 17. SQL / MySQL 18. Mobile app development 19. Python 20. MongoDB and more skills What will you get? • Responsive design on All Devices •Reusable components •Clean code •Timely delivery tested •Clear communication We would be honored to discuss your project requirements and help bring your ideas to life. Thank you for your time and consideration. Warm Regards, Toriqul Global Solutions

@slavkom1

Hi there, I read through your end‑to‑end email spam classifier request and it’s clear what you need: a clean pipeline that trains, evaluates and serves a model you can trust. I’ve built several FastAPI ML services and email/NLP classifiers, so I can handle this quickly and in a way that’s easy to maintain. I’d set it up with: • preprocessing that normalises text and builds TF‑IDF • training scripts for LR, NB and linear SVM with auto‑selection • a FastAPI /predict route returning label and probability • a metrics script and simple repo layout you can run fresh If you want the Streamlit piece, I can add it without slowing things down. I can start now and wrap it up in a few days. Do you want the model selection step to save only the best estimator, or do you prefer saving all three for later comparison? Thanks, Slavko

@AlexVydrin

Hi there! I'm really excited about the opportunity to work on your project. It sounds like a fascinating challenge, and I've got experience building similar machine learning pipelines. Here's how I plan to tackle it: First, I'll start by cleaning and preprocessing the emails, transforming them into TF-IDF vectors. Next, I'll implement training loops for Logistic Regression, Multinomial Naïve Bayes, and SVM models to find the best performer. Once that's done, I'll set up a FastAPI service that can predict spam or ham from raw email text. For evaluation, I'll create a clear notebook with all the metrics you mentioned. As for structuring the repository, it'll be organized for easy hand-off with everything documented in the README. If you're interested in adding interactivity with Streamlit, I'd love to integrate that too so you can test predictions in real-time! Let's get started; I'm eager to dive in and deliver a successful solution for you. Looking forward to working together! Best,

@Vsion2

Welcome to professional Python development services! Hi there, I'm Alema, a Python expert programmer who strives for clear code in atmospheric, numerical weather prediction, physics, and all other seminal fields. I'm ready to provide you with high-quality services. I have completed 350+ projects with a 100% Positive Rating. If you are looking for Quality work, look no further. Also, we are a team of professional workers, and we are always available 24/7 to help employers without limitations, and delivery is guaranteed on time. Your faithfully. Eng. Alema Akter

@Abuzar00

As a full stack developer with a primary focus on backend systems and strong proficiency in Python, I am well-equipped to tackle your End-to-End Email Spam Classifier project. My 5+ years of professional experience and a consistent 4.9★ rating over the course of 20+ verified client reviews speaks to my ability to deliver high-quality work within timelines and budgetary constraints.

@jayantm76

─── ⚡⭐⋆☆⋆⭐⚡ ─── Hi there, Approach: I’ll build a clean Python NLP pipeline with preprocessing, TF IDF training, model selection, FastAPI inference, metrics reporting, and an optional Streamlit demo. Timeline: I’ll deliver the core training and API flow in 3 days, then finalize evaluation, packaging, and handoff right after. I’m a strong fit for this because I build production grade Python ML pipelines that go from raw text data to reproducible model training and deployable APIs. I fit the stack with Python, scikit learn, TF IDF, Logistic Regression, Naive Bayes, linear SVM, FastAPI, and Streamlit for simple live testing. I’ve handled similar classification projects where model comparison, clean repo structure, reproducible training, clear metrics, and inference endpoints all had to be easy to rerun on a fresh environment. Your real goal is a reliable spam detection pipeline that trains from one CSV, picks the best model automatically, and exposes a clean prediction endpoint with confidence scores. So, I'll: Build the preprocessing, training, evaluation, and model selection pipeline with clear scripts, saved artifacts, and reproducible outputs. Create the FastAPI prediction endpoint, package the repo with requirements and README, and add the optional Streamlit tester if you want it included. Best, Jayant

@gazmirs

Hi, ⭐15+ Yrs Sr Developer here⭐ I’ve built end-to-end ML pipelines for text classification, including spam detection with clean preprocessing, TF-IDF, and model selection workflows. I can implement training for Logistic Regression, Naïve Bayes, and SVM, with automatic evaluation and best-model selection based on metrics like F1 and ROC-AUC. The API layer will be delivered using FastAPI, exposing a reliable /predict endpoint with probability scores and fast response times. I’ll structure the repo for easy setup, reproducibility, and clear handoff, including evaluation scripts and detailed documentation. If useful, I can also include a lightweight Streamlit interface for real-time testing and validation. If you think I am a good fit, feel free to ping me anytime. — GAZMIR

@jay8mac

Lets chat, a free consultation and no obligation. I understand you need a clean, professional, and user-friendly solution for your "End-to-End Email Spam Classifier" project. My skills in PHP, Java, JavaScript are a perfect fit for this project. While I am new to freelancer.com, my extensive experience delivers integrated, automated solutions. Regards, Jason McLachlan

@rogelitoc

Hello, I can see the challenge is to create a robust and efficient pipeline for email classification. I will implement a modular architecture where text preprocessing will handle tokenization and TF-IDF vectorization. The training loops will employ Logistic Regression, Multinomial Naïve Bayes, and linear-kernel SVM, utilizing cross-validation to select the best model. I'll ensure the FastAPI service is streamlined for /predict endpoints, returning class predictions and confidence scores. Evaluation metrics will be generated in a notebook for comprehensive performance verification, while the repository will be organized for seamless hand-off with clear documentation. The output will include a fully functional model, a FastAPI service, evaluation scripts, and a structured repository ready for deployment. I have a proven track record in building machine learning pipelines and API integrations. I can start immediately. Best Regards.

@everettj3

Hello, With over six years of experience in web development and AI, I am confident that I can deliver a top-notch and production-ready pipeline for your email spam classifier project. My proficiency in full-stack development using Python will enable quick and efficient text preprocessing, tokenization, and TF-IDF vectorization of the email bodies. I have hands-on expertise with popular ML algorithms like Logistic Regression, Naïve Bayes, and SVM that will help me train an optimal model for accurate spam/ham classification. Additionally, my skill set extends to backend development which will come in handy for setting up a FastAPI service for the project. This service will expose an efficient /predict API route enabling users to obtain predictions effortlessly. I understand the importance of reliable evaluation as well hence promise to deliver an organized evaluation notebook or script with insightful metrics like accuracy, F1, ROC-AUC, and a comprehensive confusion matrix. Crucially, my focus on documentation and structuring repositories ensures easy hand-off of the project post-delivery. I will ensure your requirements are met - from a minimal Streamlit dashboard proposal to project reproducibility and clean installation using requirements.txt. Let me bring my passion for AI-infused web development to your project; I assure you diligent work that exceeds performance expectations. Thanks!

@mikes126

1. Data Loading & Validation Load the two-column CSV (email_text, label). Check for missing values, duplicates, and class imbalance (spam vs ham). Split into train/test sets (e.g., 80/20 stratified) to preserve label distribution. 2. Text Preprocessing Pipeline Clean raw emails by: lowercasing text, removing HTML tags, URLs, numbers, punctuation, and extra whitespace. Then tokenize text and optionally remove stopwords. Apply lemmatization (better than stemming for email context). Convert processed text into numerical features using TF-IDF vectorization (unigrams + bigrams for better context capture). 3. Feature Engineering & Vectorization Use sklearn’s TfidfVectorizer with parameters like max_features (e.g., 10k–50k), ngram_range=(1,2), and min_df to reduce noise. Fit on training data only, then transform both train and test sets. 4. Model Training Pipeline Train three models: Logistic Regression (baseline strong linear model) Multinomial Naive Bayes (fast, good for text) Linear SVM (often highest accuracy for TF-IDF text) Use sklearn Pipelines to combine TF-IDF + model. Evaluate each model using cross-validation. 5. Model Selection Logic Compare models using F1-score primarily (since spam detection is often imbalanced), along with accuracy and ROC-AUC. Automatically select and persist the best model using joblib/pickle. 6. Evaluation Script / Notebook

@Zeemeeservices

Hello, With nearly a decade of experience in Full Stack and DevOps Engineering, I offer you a comprehensive set of skills that can cover every aspect of your requested project. In addition to proficiency in FastAPI and Python, I bring a deep understanding of Machine Learning (ML) techniques that will be instrumental in cleaning, tokenizing, and converting the email text to TF-IDF vectors. My extensive experience with Logistic Regression, Multinomial Naïve Bayes, and linear-kernel SVM models allows me to automate the process of selecting the best performer for your classification task. Moreover, as a ML specialist, I have hands-on experience with LLM integration libraries like OpenAI and NLP systems ideal for your spam classification job. This is complemented by my abilities as a Data Engineer and Web Scraper which will assist me heavily in setting up a robust data pipeline for your CSV file and extracting key features for training. Not only do I bring the technical know-how but also the right mindset - thinking beyond simple code and focusing on system design that guarantees reliability and long-term success. In summary, my end-to-end skills combined with genuine dedication to delivering production-ready pipelines means I am ready to take on this project immediately. I am confident that my approach will generate accurate classifiers while my streamlining expertise guarantees it will be easy for you to replicate my work on your own Thanks! Chibike