r
raihan_alii

RAIHAN

@raihan_alii

Data Scientist and Machine Learning Expert for Analytics and AI Solutions

Bangladesh
Inglese, Bengali
Alcune informazioni sono riportate in lingua inglese.
Chi sono
Hello! I am a Data Analyst and Machine Learning Engineer with a B.Sc in CSE from NITER, University of Dhaka. I specialize in turning complex datasets into actionable business insights using interactive dashboards and predictive modeling. Core Expertise: • Data Analysis & Visualization: Python (Pandas, NumPy, Seaborn), Power BI, Looker Studio, Excel. • SQL & Databases: PostgreSQL, MySQL, CTEs, Window Functions. • Automation: Google Apps Script, JavaScript. • Machine Learning: Scikit-learn, CNN, EDA.... Continua a leggere

Competenze

r
raihan_alii
RAIHAN
offline • 

Consulta i miei servizi

Machine learning
I will build machine learning and deep learning models
A partire da90 USD / progettoMaggiori dettagli
Dashboard dati
I will build custom advanced data analysis dashboard
A partire da25 USD / progettoMaggiori dettagli

Portfolio

Esperienza lavorativa

Data Science Intern

HexSoftwares • Part time

Oct 2025 - Nov 2025 • 1 mo

Worked on end‑to‑end data science projects, from understanding business requirements and exploring raw datasets to delivering reports and dashboards. Performed data cleaning, feature engineering, and EDA using Python, SQL, and Excel, then trained and evaluated classification and forecasting models. Documented a clear, reproducible workflow so results could be improved and reused for future client‑style projects. Workflow: Understand business requirements → explore raw datasets → clean data & engineer features → perform EDA in Python → train & compare ML models → select best model using metrics → create reports & dashboards for decision‑makers.

Machine Learning Intern

CodeAlpha • Part time

Jul 2025 - Sep 2025 • 2 mos

Worked on machine learning projects using algorithms such as Linear Regression, Logistic Regression, Random Forest, XGBoost, KNN, and K-Means clustering for classification, regression, and clustering tasks. Also applied train-test split, cross-validation, and model evaluation metrics to improve performance. Workflow: understand your problem → explore and clean the data (Complete Pre-process) → build and validate ML models → create clear dashboards/reports so you can take