You will get An AI-Powered Text Classifier Using Python and NLP
Project details
I help clients turn raw text data into reliable, production-ready classification models. My approach combines the speed and interpretability of classical machine learning (Logistic Regression, SVM, Naive Bayes) with the accuracy of modern transformer models like BERT — so you get the right tool for your specific use case, not a one-size-fits-all solution.
What sets me apart is that I don't just hand you a model — I show you the full picture: exploratory data analysis, side-by-side model comparisons, confusion matrices, and clear performance metrics, so you understand exactly how well the model performs and why. Every project includes clean, well-documented code you can actually use or extend.
I've built and evaluated full text classification pipelines from scratch, achieving accuracy above 95% using both classical and transformer-based approaches. Whether you need a fast, lightweight classifier or a high-accuracy deep learning solution, I'll help you find the right balance between performance, cost, and speed.
What sets me apart is that I don't just hand you a model — I show you the full picture: exploratory data analysis, side-by-side model comparisons, confusion matrices, and clear performance metrics, so you understand exactly how well the model performs and why. Every project includes clean, well-documented code you can actually use or extend.
I've built and evaluated full text classification pipelines from scratch, achieving accuracy above 95% using both classical and transformer-based approaches. Whether you need a fast, lightweight classifier or a high-accuracy deep learning solution, I'll help you find the right balance between performance, cost, and speed.
Machine Learning Tools
BERT, ChatGPT, Cloudera, GPT-3, Keras, MLflow, NumPy, pandas, Python, Python Scikit-Learn, PyTorch, SciPy, Sonnet, TensorFlowWhat's included
| Service Tiers |
Starter
$40
|
Standard
$100
|
Advanced
$200
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 days |
Number of Revisions | 1 | 2 | 2 |
Number of Model Variations | 1 | 3 | 4 |
Number of Scenarios | 1 | 1 | 2 |
Number of Graphs/Charts | 2 | 4 | 6 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | - | ||
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$20 - $40
Additional Revision
+$15
Additional Model Variation
(+ 2 Days)
+$20
Additional Scenario
(+ 2 Days)
+$15
Additional Graph/Chart
(+ 1 Day)
+$20
Data Source Connectivity
(+ 2 Days)
+$30About Abdul Raheem
Python & NLP | Text Classification, Data Cleaning, ML Pipelines
Kumasi, Ghana - 11:46 am local time
Currently completing a comparative study on misinformation detection, benchmarking classical ML models (Logistic Regression, Naive Bayes, SVM) against BERT across multiple real-world datasets (LIAR, ISOT, WELFake). This work covers the full pipeline: data cleaning, feature engineering, model training, evaluation, and deployment via a Streamlit demo.
I can help with:
• Text classification & sentiment analysis
• Data cleaning and preprocessing for ML projects
• Building and evaluating classical ML and transformer-based models
• Python scripting for data pipelines
• Web scraping and structuring unstructured text data
I’m a Computer Science student (graduating this year) with hands-on experience across the full ML workflow — not just theory. I respond fast, communicate clearly, and care about delivering clean, working code on time.
Let’s talk about what you’re trying to build.
Steps for completing your project
After purchasing the project, send requirements so Abdul Raheem can start the project.
Delivery time starts when Abdul Raheem receives requirements from you.
Abdul Raheem works on your project following the steps below.
Revisions may occur after the delivery date.
Data Review & Cleaning
I inspect your dataset, handle missing values/duplicates, and prepare it for modeling.
Exploratory Data Analysis
I analyze class balance, text patterns, and key statistics to guide model choices.