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narasimha_1801

Narasimha

@narasimha_1801

Data Analyst SQL Power BI and Excel Specialist

India
Inglese, Telugu, Kannada, Hindi
Alcune informazioni sono riportate in lingua inglese.
Chi sono
Hi, I am Narasimha, a Data Analyst skilled in SQL, Power BI, Excel, and Python. I help businesses turn raw data into meaningful insights through data cleaning, analysis, and interactive dashboards. I specialize in SQL queries, Power BI reports with DAX, advanced Excel automation, and data visualization. I have worked on Sentiment Analysis, IPL dashboards, and Customer Segmentation projects. I deliver accurate, clear, and on-time analytical solutions.... Continua a leggere

Competenze

n
narasimha_1801
Narasimha
offline • 
Tempo di risposta medio: 1 ora

Consulta i miei servizi

Analisi BI
I will design sql data warehouse and power bi dashboards
Dashboard dati
I will create professional power bi dashboard with dax and data modeling

Portfolio

Esperienza lavorativa

Data Analyst – IPL Data Analysis Project

Self Project

Dec 2025 - Feb 20262 mos

- Built an end-to-end cricket analytics platform using SQL Server and Power BI - Implemented Medallion Architecture (Bronze, Silver, Gold) for structured data processing - Developed ETL pipelines using stored procedures and BULK INSERT - Performed data cleansing, transformation, and standardization - Designed star schema with fact and dimension tables - Implemented surrogate keys for analytics-ready modeling - Connected data warehouse to Power BI for reporting - Created interactive dashboards with advanced DAX measures - Generated KPI-driven insights on batting, bowling, and team performance

Customer Churn Prediction Using Machine Learning

Independent Project

Nov 2025 - Dec 20251 mo

- Performed data preprocessing including missing value handling, encoding, and feature scaling - Conducted exploratory data analysis to identify churn patterns and key risk factors - Implemented classification models including Decision Tree, Random Forest, and XGBoost - Applied hyperparameter tuning to optimize model performance - Evaluated models using Accuracy, Precision, Recall, F1-score, and Confusion Matrix - Addressed class imbalance using resampling techniques - Identified high-risk customers based on predictive probabilities - Generated actionable insights to improve customer retention strategies