You will get analyze your data and build an accurate Machine Learning model
Project details
I create accurate Machine Learning models and insightful data analyses using Python. My projects stand out for clarity, speed, and real business value.
Machine Learning Tools
MLflow, pandas, PyMC, Python, Python Scikit-Learn, PyTorch, scikit-learn, SQL, TensorFlow, XGBoostWhat's included
| Service Tiers |
Starter
$20
|
Standard
$50
|
Advanced
$100
|
|---|---|---|---|
| Delivery Time | 5 days | 7 days | 10 days |
Number of Revisions | 1 | 2 | 3 |
Number of Graphs/Charts | 3 | 4 | 5 |
Model Validation/Testing | - | ||
Model Documentation | - | ||
Data Source Connectivity | - | - | |
Source Code | - | - |
About Ali
AI & Machine Learning | Artificial Intelligence, C++, Data Analysis
Giza, Egypt - 10:09 am local time
I am a motivated and detail-oriented AI/ML Engineer trainee specializing in Data Science and Machine
Learning. I help businesses and organizations unlock the full potential of their data by performing
in-depth analysis, building predictive machine learning models, and creating clear visualizations
that transform complex datasets into actionable insights and smarter strategies.
Work Experience
AI and Machine Learning Engineer trainee, DEPI.
Projects
1. Company Analysis & Profit Prediction
* Developed a machine learning project to analyze sales data and predict profit patterns.
* Tools: Python, Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, Plotly, PCA, Random Forest, SVC,
KNN, Logistic Regression.
2. Diabetes Prediction Using Machine Learning
* Built a machine learning pipeline to predict diabetes based on health metrics.
* Included EDA, data cleaning, feature correlation study, and class imbalance handling.
Steps for completing your project
After purchasing the project, send requirements so Ali can start the project.
Delivery time starts when Ali receives requirements from you.
Ali works on your project following the steps below.
Revisions may occur after the delivery date.
Step 1: Review client requirements
Understand the project goals, dataset details, and desired output (prediction, report, or dashboard).
Step 2: Data cleaning and preprocessing
Clean missing or duplicated data, normalize formats, and prepare the dataset for analysis.