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You will get a custom NLP text classification pipeline with clean code and frontend.
Project details
You will get a production-ready NLP text classification
pipeline built on your real-world text data.
Proven results: ~90% classification accuracy achieved
in real world projects with customer data using TF-IDF and tuned ML classifiers.
What you get:
• Text preprocessing (tokenization, stopword removal,
lemmatization, TF-IDF feature extraction)
• Multiple classifier benchmarking & selection
• Class imbalance handling
• Analytics of data with insights full charts
• Data Analytics — class distribution, word frequency,
text length analysis with charts
• WordCloud, confusion matrix & correlation heatmaps
• Full evaluation (accuracy, F1, precision, recall)
• Optional Streamlit dashboard & FastAPI endpoint
• Clean, documented Python code
Ideal for: sentiment analysis, spam detection, customer
feedback classification, or any text labeling problem.
pipeline built on your real-world text data.
Proven results: ~90% classification accuracy achieved
in real world projects with customer data using TF-IDF and tuned ML classifiers.
What you get:
• Text preprocessing (tokenization, stopword removal,
lemmatization, TF-IDF feature extraction)
• Multiple classifier benchmarking & selection
• Class imbalance handling
• Analytics of data with insights full charts
• Data Analytics — class distribution, word frequency,
text length analysis with charts
• WordCloud, confusion matrix & correlation heatmaps
• Full evaluation (accuracy, F1, precision, recall)
• Optional Streamlit dashboard & FastAPI endpoint
• Clean, documented Python code
Ideal for: sentiment analysis, spam detection, customer
feedback classification, or any text labeling problem.
Machine Learning Tools
Azure Machine Learning, ChatGPT, NLTK, NumPy, pandas, Python, Python Scikit-Learn, XGBoostWhat's included
| Service Tiers |
Starter
$20
|
Standard
$35
|
Advanced
$50
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 days |
Number of Revisions | 1 | 2 | 2 |
Number of Model Variations | 2 | 3 | 4 |
Number of Scenarios | 0 | 2 | 2 |
Number of Graphs/Charts | 5 | 8 | 10 |
Model Validation/Testing | - | ||
Model Documentation | - | ||
Data Source Connectivity | - | - | |
Source Code |
Frequently asked questions
About Muhammad
Machine Learning Engineer | DATA SCIENTIST | RAG | LangGraph | Python
Lahore, Pakistan - 3:22 am local time
My work goes beyond notebooks. I design, build, and deploy complete ML pipelines and agentic AI systems — from raw data ingestion and feature engineering to model training, optimization, and live deployment. I build agents that reason, remember, and act autonomously using LangGraph stateful workflows, tool calling, and RAG pipelines. Every project I deliver is production-ready, documented, and built to create actual business value.
𝐖𝐡𝐚𝐭 𝐈 𝐡𝐚𝐯𝐞 𝐝𝐞𝐥𝐢𝐯𝐞𝐫𝐞𝐝:
Built a Job Skill Recommender analyzing 1,500+ real job postings using cosine similarity and 93 engineered binary features — deployed live on Azure
Developed a Bank Churn Predictor with a full SMOTE pipeline, reducing false negatives on imbalanced data
Built SmartPricer, a smartphone price prediction app (R²=0.903) deployed with FastAPI
Engineered a production-ready agentic RAG chatbot using LangGraph with stateful tool calling, long-term memory, and Groq LLM integration — deployed on Hugging Face Spaces
Completed a professional NLP internship delivering a text classification pipeline at ~90% accuracy
𝐂𝐨𝐫𝐞 𝐒𝐤𝐢𝐥𝐥𝐬:
𝘗𝘺𝘵𝘩𝘰𝘯 · 𝘗𝘢𝘯𝘥𝘢𝘴 · 𝘚𝘤𝘪𝘬𝘪𝘵-𝘭𝘦𝘢𝘳𝘯 · 𝘕𝘓𝘗 · 𝘓𝘢𝘯𝘨𝘊𝘩𝘢𝘪𝘯 · 𝘓𝘢𝘯𝘨𝘎𝘳𝘢𝘱𝘩 · 𝘙𝘈𝘎 · 𝘍𝘢𝘴𝘵𝘈𝘗𝘐 · 𝘚𝘵𝘳𝘦𝘢𝘮𝘭𝘪𝘵 · 𝘍𝘦𝘢𝘵𝘶𝘳𝘦 𝘌𝘯𝘨𝘪𝘯𝘦𝘦𝘳𝘪𝘯𝘨 · 𝘌𝘋𝘈 · 𝘔𝘢𝘤𝘩𝘪𝘯𝘦 𝘓𝘦𝘢𝘳𝘯𝘪𝘯𝘨
I specialize in turning messy, unstructured data into clean insights and deployed applications — and building intelligent agents that automate complex workflows businesses previously handled manually.
𝐈𝐟 𝐲𝐨𝐮 𝐧𝐞𝐞𝐝 𝐚 𝐫𝐞𝐥𝐢𝐚𝐛𝐥𝐞 𝐌𝐋 𝐚𝐧𝐝 𝐀𝐈 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐰𝐡𝐨 𝐝𝐞𝐥𝐢𝐯𝐞𝐫𝐬 𝐞𝐧𝐝-𝐭𝐨-𝐞𝐧𝐝 — 𝐧𝐨𝐭 𝐣𝐮𝐬𝐭 𝐜𝐨𝐝𝐞, 𝐛𝐮𝐭 𝐫𝐞𝐬𝐮𝐥𝐭𝐬 — 𝐥𝐞𝐭'𝐬 𝐭𝐚𝐥𝐤.
Steps for completing your project
After purchasing the project, send requirements so Muhammad can start the project.
Delivery time starts when Muhammad receives requirements from you.
Muhammad works on your project following the steps below.
Revisions may occur after the delivery date.
Text Analytics & EDA
Class distribution, word frequency, text length charts, WordCloud — understand your data before modeling.
Text Preprocessing & Cleaning
Tokenization, stopword removal, lemmatization and TF-IDF to transform raw text into structured features.