Jalgaon, Maharashtra, India
Hi, I'm Lokesh. I'm a BCA graduate passionate about Data Analytics, Business Intelligence, and Python Development. I enjoy turning raw data into meaningful insights and building data-driven solutions using Power BI, SQL, Python, Excel, and EDA. Over the past year, I've strengthened my skills through internships with Microsoft Elevate (AICTE) and iBase Electrosoft, along with hands-on projects like the Blinkit Sales Dashboard, Mobile Sales Dashboard, and Python-based data analysis and machine learning solutions using real-world datasets. I'm currently seeking opportunities as a Data Analyst, BI Analyst, or Python Developer, where I can apply my skills, solve real business problems, and continue learning from experienced professionals. I'm always happy to connect with professionals, mentors, recruiters, and fellow tech enthusiasts.
Power BI Intern — Microsoft Elevate (AICTE) ▸ Engineered an end-to-end Business Intelligence dashboard with 5+ dynamic visualizations and KPI cards, transforming raw business data into executive-level reporting insights using Power BI and DAX. ▸ Implemented star-schema relational data model with calculated DAX measures, eliminating reporting errors across all computed fields and improving data model accuracy. ▸ Completed 10+ Microsoft Learn modules on data modeling, DAG relationships, and cloud report publishing within a structured 4-week program; selected from national applicant pool under AICTE.
Designed and delivered an end-to-end Power BI dashboard with 5+ dynamic visualizations and KPI cards, transforming raw business data into actionable reporting insights. Improved data model accuracy by implementing star-schema relationships and calculated measures using DAX, reducing reporting errors across all calculated fields. Completed 10+ Microsoft Learn modules covering data modeling, Directed Acyclic Graph (DAG) relationships, and report publishing in a structured 4-week program.
Processed and cleaned structured datasets exceeding 5,000 records using Pandas and NumPy, eliminating 15%+ data inconsistencies and improving overall data quality. Automated Python data preprocessing pipelines, reducing manual transformation effort by approximately 40% compared to spreadsheet-based workflows. Developed and evaluated baseline ML classification models using Scikit-learn, achieving measurable performance benchmarks across multiple evaluation metrics.
AI/ML Intern — iBase Electrosoft LLP ▸ Processed and cleaned structured datasets exceeding 5,000 records using Pandas and NumPy, eliminating 15%+ data inconsistencies and improving pipeline data quality for ML workflows. ▸ Automated Python data preprocessing pipelines, reducing manual transformation effort by ~40% compared to spreadsheet-based workflows — equivalent output delivered in hours instead of days. ▸ Developed and evaluated ML classification models using Scikit-learn, benchmarking accuracy, precision, and recall across multiple model iterations to identify best-performing configuration.