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rashid_793

RASHID

@rashid_793

Data Scientist

India
English
About me
Hi, I’m Rashid, a Data Science professional passionate about turning raw data into meaningful insights and practical solutions. I focus on accuracy, clarity, and delivering results that clients can actually use. I work with Python, SQL, Pandas, NumPy, Machine Learning, Power BI, Matplotlib and Seaborn. I can help with data cleaning, EDA, data visualization, predictive modeling, statistical analysis and interactive dashboards. My goal is to transform complex data into clear, actionable insights that help you make better decisions.... Read more

Skills

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rashid_793
RASHID
Offline • 

See my services

Machine Learning
I will build a machine learning model for prediction using python
From$90 / projectMore details
Product Analytics
I will do data analysis, excel report and power bi dashboard
From$25 / projectMore details

Portfolio

Work experience

Rubixe

Data Science Intern

Rubixe • Full-time

Mar 2026 - Sep 2026 • 6 mos

Completed a 6-month Data Science internship at Rubixe with a focus on the Certified Data Scientist (CDS) track. Gained hands-on experience through Proof of Concept (POC) projects, project-based assignments, and client-oriented work. Key contributions: - Applied analytical concepts to real-world business scenarios, interpreting data and generating actionable insights - Worked across Data Analytics, Business Intelligence, and Data Consulting engagements - Structured problem-solving approaches for client-facing deliverables - Bridged theoretical knowledge with practical application through supervised project work Received a certificate of internship completion, with feedback highlighting professionalism, analytical thinking, and attention to detail.

DataMites™

Machine Learning Engineer – IT Incident Priority Prediction

DataMites™ • Freelance

Mar 2026 - Sep 2026 • 6 mos

Built an end-to-end machine learning solution to predict high-priority IT incidents for a mid-sized IT service management (ITSM) operation handling 22,000–25,000 monthly tickets. - Extracted and cleaned a 46,606-record dataset from MySQL, resolving data corruption issues (locale-based number formatting) and inconsistent categorical encodings via regex-based parsing. - Conducted exploratory data analysis to uncover class imbalance (98.5% vs 1.5%), data leakage risks, and time-based collection gaps. - Engineered features from timestamps and high-cardinality categorical fields while preventing leakage from derived target variables. - Trained and compared 4 classification algorithms (Logistic Regression, Decision Tree, Random Forest, XGBoost), tuning for F1-score to balance precision/recall trade-offs under severe class imbalance. - Delivered a final XGBoost model achieving 0.926 ROC-AUC and 65% recall on high-priority incidents, with feature-importance analysis identifying banking devices and core infrastructure as leading risk categories — translated into actionable business recommendations.