Shikha Mehta

• 4.0 GPA in master's program
• Received Teaching assistant scholarship at UHD
• Mentored and taught 2500+ students
• Helped students increase their scores by almost 25% in their final board exams of class XII.

Bean Sort: Classification of Dry Beans using Machine Learning, Python

The goal of the project was to develop machine learning models that can accurately classify the different types of dry beans based on their visual features. Several machine learning algorithms were used in this project, including decision trees, random forests, support vector machines (SVM), and K-nearest neighbors (KNN).

Cafe Database Management System, SQL

Used MySQL to create and maintain database for a café to support their day-to-day operations like receiving orders and managing inventory to generating sales reports and analyzing customer feedback. Created database design, identified relationships between tables, normalized the tables.

A Comprehensive Study of Heart Disease Risk Factors Using Multivariate Analysis techniques with R Programming

Performed multivariate analysis techniques like principal component analysis and factor analysis to reduce the dimensionality of the heart Disease dataset followed by training several supervised machine learning models to predict whether a person has heart disease or not.

Visualizing World Energy Consumption using Tableau

Used Tableau to create Visualizations for energy consumption data, by their sources and countries and to illustrate the scale of usage of renewable and non- renewable energy sources. Created interactive dashboards consisting of line graphs, maps, bar graphs, lollipop chart, stacked bar chart and dough nut chart.

Time series analysis of US auto production using R Programming

Applied the American domestic auto production data set from January 1993 to September 2022 and SARIMA models to suggest an appropriate model for forecasting future values.

Predicting Life Expectancy using Multiple Linear Regression with R Programming

Developed a life expectancy prediction model using regression techniques, incorporating factors like infant deaths, adult mortality, and schooling. Applied transformations, variable selection, and model validation for the final model.