Tanvi Salunke

An ambitious and skilled Data enthusiast with comprehensive skills in data analytics, specializing in advanced SQL, BI tools like Tableau, and data storage solutions. I am committed to executing planned analyses on diverse data sources, building and iterating on dashboards, and providing actionable insights.Committed to delivering high-value results and proactive solutions to drive business success.

Sign Language Detection Using Gesture Recognition: Developed a hand gesture recognition model using CNN for real-time gesture capture and sign language interpretation, achieving a maximum accuracy of 94%. Implemented a user-friendly interface for live video frame analysis and gesture matching.

Real Estate house price predictor using Machine Learning:   Developed a machine learning pipeline for housing price prediction, involving data preprocessing, feature engineering, and model evaluation with cross-validation and stratified sampling. Implemented a RandomForestRegressor for accurate predictions and deployed the model using joblib for real-world application

Stock Market Analysis and Prediction Using LSTM: Retrieved historical stock data for major tech companies using pandas and yfinance, conducting exploratory data analysis on closing prices, trading volumes, and moving averages. Developed and evaluated an LSTM model with Keras to predict Apple's stock price, analyzing performance with RMSE and comparing predicted versus actual values.

Predicting Titanic Survivors with Ensemble Learning: Built an advanced predictive model to forecast Titanic passenger survival using feature engineering, hyperparameter tuning, and ensemble learning techniques. Combined Random Forest and Gradient Boosting classifiers to achieve high accuracy. Evaluated model performance with accuracy, classification reports, and feature importance visualization.

Data Analysis project using SQL: This project involves analyzing the world population during the year 2015 to examine various aspects of the dataset. By writing and executing SQL queries, the meaningful insights were extracted about global population distribution, and growth trends. Through various SQL queries, the project aims to understand patterns and support data-driven decision-making for demographic studies.