You will get a Custom Machine Learning Model for Classification or Prediction
Project details
You will get a custom machine learning model designed to solve your specific business or research problem using proven, industry-standard techniques. I focus on building practical, explainable, and well-evaluated models rather than black-box solutions.
Before training, I carefully analyze and preprocess your data to ensure quality and reliability. I then select the most suitable algorithm for your use case, train the model and evaluate its performance using appropriate metrics such as accuracy, precision, recall, F1-score or error rates. Clear visualizations and charts are included so results are easy to understand, even for non-technical stakeholders.
You will receive the complete source code, model files and clear documentation explaining how the model works, how results were achieved, and how it can be reused or extended. My approach emphasizes clarity, reproducibility and real-world usability.
This service is ideal for classification or prediction tasks such as customer churn analysis, risk scoring, sentiment analysis, demand forecasting or academic projects.
Before training, I carefully analyze and preprocess your data to ensure quality and reliability. I then select the most suitable algorithm for your use case, train the model and evaluate its performance using appropriate metrics such as accuracy, precision, recall, F1-score or error rates. Clear visualizations and charts are included so results are easy to understand, even for non-technical stakeholders.
You will receive the complete source code, model files and clear documentation explaining how the model works, how results were achieved, and how it can be reused or extended. My approach emphasizes clarity, reproducibility and real-world usability.
This service is ideal for classification or prediction tasks such as customer churn analysis, risk scoring, sentiment analysis, demand forecasting or academic projects.
Machine Learning Tools
BERT, Google Data Studio, Keras, NumPy, pandas, Python, Python Scikit-Learn, SciPy, TensorFlowWhat's included $100
These options are included with the project scope.
$100
- Delivery Time 5 days
- Number of Revisions 2
- Number of Model Variations 1
- Number of Scenarios 1
- Number of Graphs/Charts 3
- Model Validation/Testing
- Model Documentation
- Source Code
About Lewis
Machine Learning Engineer | Python AI Models, NLP & Computer Vision
Nairobi, Kenya - 5:42 pm local time
I specialize in applied machine learning and Python-based AI systems, focusing on solutions that move from raw data to reliable, usable and explainable outputs. My work bridges data science and backend engineering, ensuring models integrate cleanly into real applications.
I have hands-on experience building end-to-end ML pipelines, training and evaluating models and deploying AI functionality through APIs and dashboards. I prioritize maintainable code, clear results and systems that perform consistently under real-world constraints.
How I Work With Clients
🔹 Machine Learning Model Development
I develop machine-learning models from data ingestion and preprocessing through training, evaluation, tuning, and deployment-ready outputs using proven ML workflows rather than ad-hoc experimentation.
🔹 AI Backends & API-Driven Systems
I design Python backends with FastAPI to serve trained models, handle incoming data (including images and text), and return structured predictions with confidence scores.
🔹 Data Analysis & Feature Engineering
I transform structured and semi-structured data (CSV, GeoJSON, satellite data) into meaningful features that improve model performance and interpretability.
What Differentiates My Approach
⚡ Strong ML Fundamentals
I follow disciplined workflows: data cleaning, EDA, feature engineering, model selection, evaluation and iteration ensuring results are measurable and defensible.
⚡ Production-First Thinking
I design models with deployment in mind, considering latency, reliability, interpretability and integration from the start.
⚡ Clear Communication
I document assumptions, explain results, and highlight limitations so both technical and non-technical stakeholders understand how the system behaves.
Technical Expertise
Machine Learning & AI
• Supervised learning (classification & prediction)
• Model training, evaluation, and optimization
• Natural Language Processing (NLP)
• Computer vision and image-based inference
• Responsible AI and real-world constraints
Data Science & Analytics
• Data preprocessing and cleaning
• Exploratory Data Analysis (EDA)
• Statistical analysis (univariate & multivariate)
• Data visualization and trend analysis
• Clustering and topic modeling (LDA)
NLP & Advanced Models
• Text classification (spam detection, content moderation)
• BERT-based models and embeddings
• Feature extraction and semantic analysis
Python & ML Tooling
• Python (clean, maintainable, production-ready code)
• Scikit-learn, TensorFlow, and ML libraries
• Google Colab for experimentation and training
• Google Earth Engine for satellite-based analysis
Geospatial & Predictive Modeling
• NDVI and rainfall data analysis
• Spatial datasets (GeoJSON)
• Hotspot detection and forecasting
• Interactive dashboards with Streamlit
• Map-based visualization of predictions
ML Deployment & Integration
• FastAPI-based model serving
• API-driven inference pipelines
• Model-to-application integration
• Dashboard-driven decision support
What You Can Expect
✔️ Well-structured, production-ready ML workflows
✔️ Models built with real data and realistic constraints
✔️ Readable, reusable Python code
✔️ Honest feedback on feasibility and performance
✔️ A long-term mindset focused on improving and maintaining models
If you’re looking for support with machine-learning models, AI-powered APIs, data analysis or Python-based AI systems, let’s talk.
Steps for completing your project
After purchasing the project, send requirements so Lewis can start the project.
Delivery time starts when Lewis receives requirements from you.
Lewis works on your project following the steps below.
Revisions may occur after the delivery date.
3
I clean and preprocess the data, train the machine learning model, evaluate performance, and prepare the final deliverables.