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Shraddha Nair

@shraddhanair

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Detail-oriented Data Analyst specializing in Python, SQL, Excel, and Power BI to drive insights.

India

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What I'm looking for

I’m looking for an entry-level Data Analyst role where I can build KPI dashboards, run EDA, and apply predictive modeling to deliver clear business insights. I want a collaborative team and real datasets that help me grow end-to-end.

I’m a detail-oriented Data Analyst who turns complex datasets into actionable business insights through data cleaning, visualization, and statistical analysis. I work end-to-end on analytical solutions, pairing strong communication with a quick-learning mindset in fast-paced environments.

In my projects, I built an NLP-based sentiment classification model for Marathi text, achieving 85% accuracy using LSTM and Naive Bayes, with Devanagari-specific preprocessing. I also developed predictive models for cricket match outcomes and player performance (R² of 0.99), and created predictive maintenance models (Logistic Regression, Random Forest, XGBoost) that improved recall by 20–25% and reduced downtime by 15%.

I’m confident across SQL, Python, and reporting workflows, using Power BI, Tableau, and Advanced MS Excel features (like SUMIFS, XLOOKUP, INDEX-MATCH, and pivot tables) to produce KPI dashboards, variance analysis, forecasting, and profitability insights. I’ve strengthened my foundation with hands-on data science training in Pandas, NumPy, Matplotlib, and Seaborn, plus SQL and dashboard-focused LinkedIn case studies.

Experience

Work history, roles, and key accomplishments

Predictive Maintenance Model

Predictive Maintenance of Industrial Equipment

Jan 2026 - Mar 2026 (2 months)

Created predictive maintenance models (Logistic Regression, Random Forest, XGBoost) to forecast equipment failures, improving recall by 20–25%. Used data preprocessing with SMOTE for class imbalance and identified key risk-driving features to help reduce downtime by 15%.

Python Logistic Regression Random Forest XGBoost Predictive Maintenance SMOTE data preprocessing Feature Engineering

Marathi Sentiment Analysis

Sentiment Analysis in Marathi

Aug 2025 - Mar 2026 (7 months)

Built an NLP-based sentiment classification model for Marathi text, achieving 85% accuracy by classifying reviews into positive, negative, and neutral. Implemented Devanagari-specific preprocessing (tokenization and stop-word removal) and designed an extendable framework for other Indian languages.

Python NLP Tokenization Machine Learning

Cricket Outcome Prediction

Predictive Analysis in Cricket

Jan 2025 - May 2025 (4 months)

Developed machine learning models to predict match outcomes and player performance, reaching an R² score of 0.99. Conducted EDA and feature engineering using historical cricket statistics to generate analytics for data-driven team strategy.

Python Random Forest Feature Engineering predictive modeling Sports Analytics Data Visualization

Education

Degrees, certifications, and relevant coursework

Usha Mittal Institute of Technology, SNDT Women’s University

Bachelor of Technology (B.Tech) in Data Science, Data Science

Grade: CGPA: 6.79/10 (through 6th semester)

B.Tech in Data Science at Usha Mittal Institute of Technology (SNDT Women’s University), Mumbai. CGPA is 6.79/10 through the 6th semester with final-year results pending; expected graduation is 2026.