You will get a high accuracy text classification model
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Project details
I help businesses turn messy text into actionable insights using AI. My text classification services are reliable, accurate and fully tested, with clear documentation to make results easy to use.
What I offer:
Accurate classification for small to large datasets
Multi-class, multi-label and hierarchical categorization
Preprocessing, cleaning and model validation included
CSV outputs, detailed reports and easy to read documentation
Fast turnaround and scalable results you can trust
What I offer:
Accurate classification for small to large datasets
Multi-class, multi-label and hierarchical categorization
Preprocessing, cleaning and model validation included
CSV outputs, detailed reports and easy to read documentation
Fast turnaround and scalable results you can trust
Machine Learning Tools
Amazon SageMaker, BERT, ChatGPT, GitHub Copilot, Google Sheets, GPT-3, Keras, MATLAB, Microsoft Excel, MLflow, NLTK, NumPy, OpenCV, pandas, Python, Python Scikit-Learn, PyTorch, scikit-learn, SPSS, SQL, Tableau, TensorFlow, TextBlob, Word2vec, XGBoostWhat's included
| Service Tiers |
Starter
$175
|
Standard
$375
|
Advanced
$575
|
|---|---|---|---|
| Delivery Time | 7 days | 10 days | 15 days |
Number of Revisions | 0 | 1 | 2 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 0 | 2 | 3 |
Number of Graphs/Charts | 1 | 3 | 4 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | - | ||
Source Code |
Optional add-ons
You can add these on the next page.
Additional Scenario
(+ 3 Days)
+$75
Additional Graph/Chart
(+ 2 Days)
+$75
Data Source Connectivity
(+ 3 Days)
+$75Frequently asked questions
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Awais was very helpful to our team in creating a process to extract and understand data of CGSpace, which enabled us to access the content of thousands of documents in order to power our NLP tools, he worked very closely with multiple teams, including the Performance and Results Management System team to coordinate on data formats and technical requirements. Awais was also key in ensuring our Quality Assurance process took place by supporting the team in prompting, conducting experiments and communicating with stakeholders around the specifics of the process, to enable the use of AI in the analysis of documents for QA. His knowledge on AI and various tools was very helpful to support various different smaller tasks.
About Awais
AI & ML Engineer | Data Scientist | LLMs | AI Agents | NLP | RAG
100%
Job Success
Lahore, Pakistan - 1:57 am local time
From forecasting systems to LLM pipelines and autonomous agents built for real world problems where off-the-shelf solutions fail.
The tools change with every project. The bar doesn't.
Here is an overview of my Stack
𝗠𝗟 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀:
PyTorch, TensorFlow, Scikit-learn, XGBoost, LightGBM, CatBoost, statsmodels
𝗟𝗟𝗠𝘀 & 𝗡𝗟𝗣:
Open AI, Claude, Gemini, Grok, LLaMA, Mistral, DeepSeek, BERT, BART, SetFit, HuggingFace
𝗔𝗴𝗲𝗻𝘁𝗶𝗰 & 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻:
LangChain, LangGraph, RAG Pipelines, n8n, Make, OpenAI API, Anthropic API, Lovable, OpenClaw
𝗩𝗲𝗰𝘁𝗼𝗿 & 𝗦𝗲𝗮𝗿𝗰𝗵:
Pinecone, FAISS, ChromaDB, SentenceTransformers, Embeddings
𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴:
pandas, NumPy, Parquet, Airflow, dbt, ETL Pipelines
𝗔𝗣𝗜𝘀 & 𝗦𝗰𝗿𝗮𝗽𝗶𝗻𝗴:
FastAPI, Flask, WebSocket, PRAW, BeautifulSoup, Selenium
𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻:
Matplotlib, Seaborn, Plotly, Tableau, PowerBI, SHAP
𝗖𝗹𝗼𝘂𝗱 & 𝗜𝗻𝗳𝗿𝗮:
AWS EC2, SageMaker, AWS Bedrock, Firebase, Docker, VPS
𝗙𝗿𝗼𝗻𝘁𝗲𝗻𝗱 & 𝗔𝗽𝗽𝘀:
React, Next.js, Streamlit, Gradio, Lovable
𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻𝘀:
Gmail API, Google Calendar API, WhatsApp API, Stripe, PayPal, Odoo
You can get a feel for the work pretty quickly. Here's a slice.
→ 𝗔𝗜 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 & 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗦𝘆𝘀𝘁𝗲𝗺𝘀
• Built a 𝒇𝒖𝒍𝒍-𝒄𝒚𝒄𝒍𝒆 𝑨𝑰 𝒉𝒊𝒓𝒊𝒏𝒈 𝒑𝒊𝒑𝒆𝒍𝒊𝒏𝒆 using n8n to orchestrate OpenAI-powered resume parsing with Gmail, Google Sheets, and Calendar APIs reducing 𝐻𝑅 𝑚𝑎𝑛𝑢𝑎𝑙 𝑤𝑜𝑟𝑘𝑙𝑜𝑎𝑑 𝑏𝑦 80% with centralized candidate tracking and automated scheduling.
• Developed 𝒂 𝒓𝒆𝒂𝒍-𝒕𝒊𝒎𝒆 𝑨𝑰 𝒗𝒐𝒊𝒄𝒆 𝒂𝒈𝒆𝒏𝒕 supporting voice-to-voice, speech-to-text and text-to-text conversations via FastAPI and WebSocket with ultra low latency using GPT for dialogue management.
• Built an 𝑨𝑰 𝒑𝒐𝒘𝒆𝒓𝒆𝒅 𝒕𝒆𝒍𝒆𝒎𝒆𝒅𝒊𝒄𝒊𝒏𝒆 𝒑𝒍𝒂𝒕𝒇𝒐𝒓𝒎 on Next.js and Firebase with role-based AI prompts, automated symptom collection and 𝑟𝑒𝑎𝑙 𝑡𝑖𝑚𝑒 𝑐𝑙𝑖𝑛𝑖𝑐𝑎𝑙 𝑖𝑛𝑠𝑖𝑔ℎ𝑡𝑠 for patient doctor interaction.
→ 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 & 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴
From pharmaceutical supply chains to crypto markets, I build forecasting systems that drive real inventory, budget and trading decisions.
• Built a 3𝑴+ 𝒓𝒆𝒄𝒐𝒓𝒅 𝒑𝒉𝒂𝒓𝒎𝒂 𝒇𝒐𝒓𝒆𝒄𝒂𝒔𝒕𝒊𝒏𝒈 𝒔𝒚𝒔𝒕𝒆𝒎 pipeline: XGBoost R²=0.90, 20% accuracy gain, 17-chart EDA uncovering SKU concentration risk and billing-cycle demand patterns
• 𝑪𝒓𝒄𝒓𝒚𝒑𝒕𝒐 𝒇𝒐𝒓𝒆𝒄𝒂𝒔𝒕𝒊𝒏𝒈 𝒎𝒐𝒅𝒆𝒍𝒔 using ARIMA + Reddit sentiment (PRAW + SetFit) → BUY/SELL/HOLD signals for BTC, ETH, SOL, DOGE
• 𝑫𝒆𝒎𝒂𝒏𝒅 𝒇𝒐𝒓𝒆𝒄𝒂𝒔𝒕𝒊𝒏𝒈 𝒑𝒊𝒑𝒆𝒍𝒊𝒏𝒆 (LR, XGBoost, RF, LSTM) achieving R²~0.99 used car price prediction deployed via Flask
→ 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 & 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝗮𝗹 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴
I build classification, regression, and validation systems with rigorous evaluation not just accuracy scores but defensible, 𝒑𝒓𝒐𝒅𝒖𝒄𝒕𝒊𝒐𝒏-𝒓𝒆𝒂𝒅𝒚 𝒎𝒐𝒅𝒆𝒍𝒔.
• SVM, Gradient Boosting, MLP, XGBoost, Logistic Regression always with GridSearch and KFold CV for hyperparameter integrity
• Diabetes detection: 86% accuracy on 3-class imbalanced clinical dataset with feature engineering and undersampling experiments
→ 𝗡𝗟𝗣 & 𝗟𝗟𝗠-𝗣𝗼𝘄𝗲𝗿𝗲𝗱 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲
I combine classical text modeling with modern LLMs to extract structured insight from unstructured data at scale.
• Claude 3.5 Sonnet (AWS Bedrock) + BART MNLI + SentenceTransformer pipeline quantifying open ended survey sentiment for fragrance product strategy
• Real-time Reddit 𝒔𝒆𝒏𝒕𝒊𝒎𝒆𝒏𝒕 𝒅𝒂𝒔𝒉𝒃𝒐𝒂𝒓𝒅 for ASTS ticker upvote-weighted transformer scoring with daily trend visualization
• 𝑻𝒆𝒙𝒕 𝑪𝒍𝒂𝒔𝒔𝒊𝒇𝒊𝒆𝒓 across disaster tweets (TFIDF, 80%), IMDB reviews (LSTM, 86%) and news categorization (CNN + GloVe, 75%)
• GPT-4o, Claude, LLaMA, Grok and Mistral used as deliberate data enrichment and annotation tools inside ML pipelines
I work with startups building their first AI product, enterprises with complex data problems, and individuals with unique challenges nobody else wants to touch.
If the problem is hard and the data is messy that's exactly where I do my best work.
Send me a message and let's figure out if I'm the right fit. I will tell you within 24 hours whether I can help and how.
Steps for completing your project
After purchasing the project, send requirements so Awais can start the project.
Delivery time starts when Awais receives requirements from you.
Awais works on your project following the steps below.
Revisions may occur after the delivery date.
Data Cleaning & Preprocessing
I will clean, normalize, and tokenize your dataset, handling duplicates, noise, and class imbalance as needed.
Model Development
I will build your chosen model (TF-IDF ML model or Transformer model), train it, and optimize its hyperparameters.