About Me
Analytical Statistics Graduate with a strong foundation in data mining, interpretation, and statistical modeling. Proficient in Python, R, SPSS, and Excel, transforming complex datasets into actionable insights. Passiona…
Analytical Statistics Graduate with a strong foundation in data mining, interpretation, and statistical modeling. Proficient in Python, R, SPSS, and Excel, transforming complex datasets into actionable insights. Passionate about applying machine learning and survey data analysis to solve real-world problems and support data-driven decisions.
Experience
Enumerator / Data Collector
Conducted household surveys to collect and verify socio-economic data using digital tools, ensuring accuracy and confidentiality. Collaborated with team members to meet daily targets while maintaining high data quality standards.
Post-graduate Researcher
Merging data Using R and analyze data using Python, Data analysis by using statistic and R software, Applied Machine Learning models (to predict child mortality in Punjab, Pakistan)
Virtual Internee
Conducted awareness campaigns on environmental protection and climate change as part of the Clean Green Punjab initiative. Educated communities on sustainable practices, waste management, and the importance of environmental conservation. Assisted in organizing awareness sessions and distributed educational materials to promote public participation in green initiatives.
Enumerator / Data Collector
Worked on Punjab Socio-Economic Registry (PSER).
Virtual Internee
Completed a 3-month internship.
Performed field work.
Post-graduate Researcher
Merged data using R.
Analyzed data using Python.
Performed data analysis using statistics and R software.
Applied machine learning models to predict child mortality in Punjab, Pakistan.
Undergraduate Research Assistant
Performed data analysis and interpretation using different software.
Conducted a seminar on Logistic Regression.
Undergraduate Research Assistant
Data analysis and interpretation using different software, Seminar on Logistic Regression
PROJECTS
Machine Learning-Based Prediction of Child Mortality in Punjab, Pakist
Applied machine learning algorithms including Random Forest, SVM, and KNN to predict child mortality using Multiple Indicator Cluster Survey (MICS) data. Performed extensive data cleaning, feature engineering, and predictive modeling. Used Python and SPSS for analysis. Achieved high accuracy in demographic forecasting.