Data Analyst Machine Learning

Swarada Joshi

Pune, Maharashtra +91-762******* *******.*****.**@*****.***

Linkdein: swarada-joshi-data20 GitHub: SwaradaJoshi20 Summary:

Detail-oriented Data Analyst with a M.Sc. in Statistics and experience in transforming complex data into actionable insights. Skilled in SQL, Python, and Power BI for data querying, visualization, and dashboard development. Proven ability to clean, validate, and analyze large datasets to drive business decisions. Seeking a Data Analyst role to leverage analytical skills for data-driven decision-making. Experience:

Data Analysis and Statistics Intern Pulmocare Research and Education Foundation, Pune Jan 2025 – Mar 2025

- Applied statistical and machine learning techniques for exploratory data analysis in a clinical setting.

- Utilized Python and R for data preprocessing and analysis, supporting research objectives. Education:

M.Sc. Statistics PES’S Modern College, Pune 2023–2025 CGPA: 8.59 B.Sc. Statistics Sir Parashurambhau College, Pune 2020–2023 CGPA: 8.08 Projects:

Water Level Prediction and Severity Analysis for Flood Management Python, R water-level-forecasting

- Engineered a logistic regression model to predict critical flood events with 99.97% accuracy (AUC).

- Forecasted water levels 30 days in advance using ARIMA time series modeling.

- Utilized K-Means clustering to identify 3 distinct risk-level zones for optimized resource allocation.

Survival Analysis of Liver Disease Patients Python, R Liver-disease-survival-analysis

- Built a Random Forest classifier to predict patient survival outcome with 99.24% accuracy.

- Performed feature importance analysis to identify key clinical predictors influencing mortality risk.

- Tools: Scikit-learn, Pandas, Matplotlib, Survival Analysis

Zoo Animal Classification using Machine Learning Python

- Implemented and evaluated 5 machine learning algorithms (KNN, Logistic Regression, Decision Tree, Random Forest, Naive Bayes).

- Compared model performance, identifying Random Forest and Naive Bayes as the most accurate (93.5% and 95.2% accuracy).

Technical Skills:

Programming & Data Science: Python (NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch), R, SQL

Machine Learning & AI: Regression, Classification, Forecasting, Survival Analysis, NLP (basics)

MLOps & Deployment: Model deployment (Flask, FastAPI), Docker, Git, CI/CD pipelines (GitHub Actions, Jenkins

– project-level exposure)

Visualization: Power BI, Matplotlib, Seaborn, Plotly

Other Tools: n8n (automation), Excel, MS Office

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