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Welcome to my space

Transforming Raw Data into Actionable Insights

Hi, I'm a Data Analyst passionate about uncovering hidden stories within datasets. I specialize in bridging the gap between complex engineering architectures and high-level business strategy.

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About Me

Data-driven BIM student and aspiring Data/ML Engineer with hands-on experience in statistical analysis, exploratory data analysis (EDA), machine learning, and data visualization using Python, Scikit-Learn, Pandas, NumPy, Matplotlib, and Power BI. Skilled at transforming raw datasets into actionable insights, building predictive models, and deploying machine learning applications via interactive web interfaces and Jupyter Notebook workflows.

Complementary background in REST API development and full-stack engineering strengthens ability to work across the complete data lifecycle – from backend data ingestion, pipeline optimization, and database querying to model training, evaluation, and interactive visualization. Recognized technical achiever and detail-oriented problem-solver committed to driving data-driven decision-making

Opinions argue. Data decides.

Credentials

  • Bachelor of Information Management

    Tribhuvan University, 2022-2026

  • Data Analyst Associate

    DataCamp, 2026

  • The Fundamentals of Digital Marketing

    Google Digital Garage, 2023

Core Technical Skills

Languages & Querying

Python SQL (PostgreSQL, MySQL) Pandas & NumPy JavaScript (ES6) Node.js & Express.js RESTful APIs & JSON

Machine Learning & Modeling

Scikit-Learn Predictive Modeling Exploratory Data Analysis (EDA) Feature Scaling & Encoding NLP (TF-IDF, Cosine Similarity) Hyperparameter Tuning

BI & Visualization

Tableau Desktop Power BI Seaborn & Matplotlib Plotly KPI Dashboards & DAX Data Storytelling

Deployment & Tools

FastAPI Streamlit Git & GitHub Render Deployment Jupyter Notebooks

Featured Projects

Every dataset has a story. I find it, shape it, and turn it into insight.

Nepali Movie Recommendation System Streamlit Interface
NLP & Content-Based Filtering

Nepali Movie Recommendation System

Built an end-to-end recommendation engine tailored for Nepali cinema using TF-IDF vectorization and Cosine Similarity on processed movie metadata. Integrated TMDb & OMDb APIs for live movie posters and IMDb ratings, backed by a Render web service and deployed on Streamlit Cloud.

Python (Pandas, NumPy) Scikit-Learn (TF-IDF) Streamlit TMDb / OMDb APIs Render API
GitHub Repo Live App
Nepal Premier League Predictor Dashboard Screenshot
Cricket Analytics & Predictive Modeling

NPL Predictor | Nepal Premier League Analytics

Developed an interactive web analytics portal to forecast match outcomes and evaluate team performance across the Nepal Premier League. Features real-time ML win probability estimation, toss-condition impact weighting, dynamic team asset rendering, and Plotly squad comparisons.

Python (Pandas) Scikit-Learn Plotly Streamlit
GitHub Repo Live Demo
Mental Health Signal App Screenshot
Applied Machine Learning & Web APIs

Mental Health Signal | Student Wellness Analytics

Built an end-to-end predictive web application analyzing student daily habits, digital screen time, and physiological stress factors[cite: 1]. Trained a supervised regression model and deployed a high-performance FastAPI backend connected to an interactive real-time gauge dashboard[cite: 1].

Python (Scikit-Learn) FastAPI JavaScript (ES6) REST API
GitHub Repo Live Demo
Customer Engagement Dashboard Screenshot
Business Intelligence & Visual Analytics

Customer Behavior Engagement Analytics

Designed an interactive Tableau dashboard to analyze retail customer engagement and retention trends[cite: 1]. Transformed multi-source transactional data using SQL and Pandas into executive key metrics, tracking conversion funnels and an 89.5% retention benchmark[cite: 1].

Tableau Desktop SQL (PostgreSQL) Python (Pandas)
GitHub Repo
Heart Disease Risk Prediction App Screenshot
Clinical Machine Learning & Risk Diagnostics

Heart Disease Risk Prediction System

An end-to-end predictive healthcare application for real-time cardiovascular risk assessment[cite: 1]. Achieved 88.3% classification accuracy by performing data scaling, feature engineering, and hyperparameter tuning inside Scikit-Learn pipelines, deployed via Streamlit[cite: 1].

Python (Scikit-Learn) Streamlit Pipeline Tuning
GitHub Repo
Titanic Survival Prediction Analysis Screenshot
Exploratory Data Analysis & Statistical Modeling

Titanic Survival Driver Audit

A complete statistical audit and visualization study analyzing passenger demographics[cite: 1]. Performed missing value imputation, feature engineering, and hypothesis testing to identify demographic drivers (socioeconomic class, age, gender) influencing survival rates[cite: 1].

Python (Seaborn, Matplotlib) Statistical EDA Feature Engineering
GitHub Repo

Let's Connect

I'm currently looking for new opportunities and data challenges. Drop an email or reach out via professional networks!