Ronish Lamsal

Analytical professional with expertise in data analysis, statistical modeling, and machine learning, built on a solid foundation in engineering problem-solving. Skilled in leveraging tools like Python, R, and SQL to extract insights, develop predictive models, and optimize processes. Adaptable and quick to learn, with a passion for transforming complex data into actionable outcomes to drive success.

• Engineer-In-Training Certification (State of California)
• ICC Reinforced Concrete Inspector
• Nuclear Gauge Operator Training
• ACI Concrete Field Testing Grade 1 Certification

Online Retail - Customer Segmentation (https://github.com/ronishlamsal/Online-RetailCustomer-Segmentation-Cross-Selling-)

• Developed customer segmentation using K-Means clustering. Analyzing RFM (Recency, Frequency, and Monetary value) metrics to identify high-value customer groups.,
• Implemented FP-Growth for market basket analysis, generating cross-selling recommendations to increase average transaction value.,
• Aligned machine learning insights with business strategies to optimize customer retention and boost revenue through targeted marketing campaigns.

Predicting Credit Card Default, (https://github.com/ronishlamsal/Predicting_Credit_Card_Default)

• Preprocessed and analyzed financial data using Python libraries (pandas, numpy) to prepare for predictive modeling.,
• Visualized key insights and patterns using seaborn and matplotlib to identify influential factors in customer default behavior.,
• Built and evaluated machine learning models, including Logistic Regression, SVM, Random Forest, and XGBoost, to predict credit card payment defaults., Applied clustering techniques (KMeans) to segment customers based on risk profiles, supporting targeted risk management strategies.,
• Improved model accuracy through hyperparameter optimization and cross-validation, achieving robust performance on test datasets.