You will get machine learning model for your dataset using Python
Project details
You will receive a complete, well-documented machine learning solution tailored to your dataset and business needs. I provide high-quality data preprocessing, exploratory data analysis (EDA), feature engineering, model development, validation, and performance evaluation using Python and industry-standard libraries such as Pandas, NumPy, Scikit-learn, and XGBoost. Every project includes clean, readable source code and clear documentation to ensure you can understand and use the results with confidence. Whether you need a classification, regression, clustering, or recommendation model, I focus on delivering accurate, reliable, and efficient solutions that help you make data-driven decisions. Your satisfaction, code quality, and timely delivery are my top priorities.
Machine Learning Tools
Azure Machine Learning, ChatGPT, Databricks MLflow, GitHub Copilot, Google AutoML, GPT-3, Microsoft Excel, Minitab, NumPy, Python, scikit-learn, XGBoostWhat's included
| Service Tiers |
Starter
$50
|
Standard
$80
|
Advanced
$120
|
|---|---|---|---|
| Delivery Time | 1 day | 3 days | 5 days |
Number of Revisions | 1 | 2 | 3 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | - | ||
Source Code | - | - |
Optional add-ons
You can add these on the next page.
Additional Graph/Chart
(+ 1 Day)
+$10
Deployment
(+ 2 Days)
+$30About Bassem
Data Scientist | Machine learning , Artificial Intelligence
Alexandria, Egypt - 4:15 am local time
Computer Science student specializing in Intelligent Systems and Artificial Intelligence, with
practical experience building end-to-end machine learning pipelines across supervised learning,
unsupervised learning, deep learning, and recommendation systems. Proficient in Python,
Scikit-learn, TensorFlow, and Streamlit, with exposure to classical AI methods including expert
systems, fuzzy logic, and genetic algorithms. Familiar with NLP techniques such as TF-IDF and
Bag-of-Words. Seeking a Machine Learning internship to apply analytical skills to real-world AI
challenges.
PROJECTS
Hybrid Movie Recommendation System | Python, Scikit-learn, Surprise, Streamlit
* Built a hybrid engine on MovieLens 100K combining TF-IDF + cosine similarity (content-based)
with SVD matrix factorization (collaborative filtering) via weighted averaging.
* Evaluated with RMSE, MAE, Precision, Recall, and F1-Score; deployed an interactive Streamlit UI
Steps for completing your project
After purchasing the project, send requirements so Bassem can start the project.
Delivery time starts when Bassem receives requirements from you.
Bassem works on your project following the steps below.
Revisions may occur after the delivery date.
Project Review
Review your requirements, dataset, and project objectives.
Data Preparation
Clean, preprocess, and prepare the dataset for machine learning.