SHASHANK SHARMA

Performed extensive data preprocessing, cleaning, and manipulation using Python with Pandas to prepare a comprehensive dataset from multiple sources.

Executed exploratory data analysis (EDA) to identify critical trends in sales performance, top-selling products, and customer behavior.

Developed an interactive dashboard using Power BI, visually presenting findings to enable stakeholders to make informed, data-driven decisions.

Developed a predictive model using Scikit-learn and Linear Regression on the Bangalore home prices dataset, incorporating data cleaning, outlier detection, feature engineering, and hyperparameter tuning (GridSearchCV).

Deployed the trained model via a Python Flask server, effectively handling HTTP requests and enabling real-time price predictions.

Built a responsive frontend using HTML, CSS, and JavaScript, allowing users to input property details and receive immediate price estimations.

Improved model accuracy through K-Fold cross-validation and rigorous data preprocessing, ensuring reliable predictions.

Engineered a machine learning model to predict IPL match winning probabilities, leveraging real-time match statistics, team performance, and game progress.

Utilized Python with NumPy and Pandas for data processing and feature engineering to build a robust predictive system.

Provided actionable insights into team performance and match conditions, enhancing strategic understanding of game dynamics.