Machine Learning Data Science

KAMAL SALTANI

LinkedIn: www.linkedin.com/in/kamal-saltani-5b6b79316 Email: ************@*****.***

GitHub : https://github.com/saltanikamal/DataSciencePortfolio Mobile: 571-***-****

PROFESSIONAL SUMMARY

Master of Science in Data Science with a strong foundation in statistical analysis and machine learning. Proficient in Python, SQL, and cloud environments. Experienced in developing predictive models using logistic regression, XGBoost, neural networks, and more. Committed to leveraging data insights for business optimization.

TECHNICAL SKILLS

Programming Languages: Python, SQL, R

Machine Learning Techniques: Logistic Regression, Random Forest, XGBoost, Neural Networks, NLP, K-means Clustering, ARIMA, Prophet Forecasting

Data Tools & Platforms: Jupyter Notebook, Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch

Cloud Environments: Azure, Databricks, AWS, Hadoop

Data Visualization: Tableau, Looker Studio

Model Deployment: Flask/Django for APIs, Docker, Kubernetes

Version Control: Git

Other Tools: Power BI, Excel, Google Sheets

EDUCATION

Lewis University, Romeoville, Chicago, IL

Master’s in data science, Life Science; GPA: 3.8 December 2018 - August 2021

Massachusetts Institute of Technology(MIT), Cambridge, Massachusetts

Professional Certificate in Data Science and Analytics; GPA: 4.00 August,2024 - February 2024

Masters in Statistic

ENSAI – National School for Statistics and Information Analysis, France November 1998

Relevant Coursework:

Mathematical Modeling

Advanced Machine Learning

Cloud Computing and Big Data

Predictive Analytics

PROFESSIONAL EXPERIENCE

Collaborate with senior data scientists to design and implement predictive models for enhancing digital offerings.

Develop Python scripts for data exploration and preprocessing, preparing datasets for model training.

Apply machine learning techniques such as logistic regression and XGBoost to solve classification problems.

Deploy models using Flask/Django APIs and integrate them into cloud environments like AWS or Azure.

Create visualizations in Tableau to communicate insights effectively to non-technical stakeholders.

Conducted exploratory data analysis using Python libraries like Pandas and NumPy.

Built clustering models using K-means to segment customers for targeted marketing strategies.

Improved model accuracy by 30% through hyperparameter tuning and feature engineering.

PROJECTS

Predictive Customer Churn Model

Developed a predictive model using logistic regression to forecast customer churn.

Integrated the model into a cloud-based system for real-time predictions.

Achieved an accuracy of 85% and reduced customer churn by 20%.

Sentiment Analysis on Social Media

Built an NLP model using TensorFlow to analyze social media sentiment.

Presented findings through interactive dashboards in Tableau.

More projects are listed in my Portfolio: https://github.com/saltanikamal/DataSciencePortfolio

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