You will get Machine Learning Prediction Model Development (Python)
Project details
You will get a complete Machine Learning prediction/classification system (e.g breast cancer prediction ) built in Python. I deliver clean, well-documented code, processed datasets, and detailed reports with charts and metrics. What makes this project unique is the flexibility: you can choose between code-only, analysis report, or even a simple GUI for easy use. My focus is on accuracy, usability, and delivering solutions tailored to your data and goals.
Machine Learning Tools
NumPy, pandas, Python Scikit-Learn, scikit-learn, TensorFlowWhat's included
| Service Tiers |
Starter
$25
|
Standard
$50
|
Advanced
$100
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 1 | 3 | 5 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | - | - | |
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$25 - $50
Additional Revision
+$15
Additional Model Variation
(+ 2 Days)
+$30
Additional Graph/Chart
(+ 1 Day)
+$15
Model Documentation
(+ 2 Days)
+$40Frequently asked questions
About Nada
AI & Machine Learning | Artificial Intelligence, Computer Vision, IDE
Assiut, Egypt - 10:38 pm local time
*Skilled in Python, TensorFlow , Mobile net , OpenCV, fine tune pre-trained model and Deep Learning
Expertise in building AI models for face recognition, emotion detection, OCR, object detection, and recommendation systems
Experience running optimized models on Raspberry Pi and edge devices
Full project lifecycle management — from data collection and model training to deployment and integration
Clear and consistent communication is important to me, so let’s stay connected.
Steps for completing your project
After purchasing the project, send requirements so Nada can start the project.
Delivery time starts when Nada receives requirements from you.
Nada works on your project following the steps below.
Revisions may occur after the delivery date.
Receive Data & Initial Assessment
Inspect the provided dataset or sample, confirm format, check missing values/quality, and confirm project goal and deliverables.
Data Cleaning & Preprocessing
Clean missing values, encode categorical features, scale/normalize if needed, and split the data into training/validation/test sets.