Semester 4

Title:  Exploratory Data Analysis on Uber Data 

Synopsis: This project aims to conduct an exploratory data analysis (EDA) on Uber data from New York City (NYC) during the period of April 2014 to September 2014. The project will leverage this dataset to uncover patterns, trends, and potential correlations, offering valuable insights into Uber's operations and their impact on the urban transportation landscape. The project will begin by collecting a comprehensive dataset from Uber's operations in NYC during the specified time frame. Next, the project will focus on preprocessing the raw data, which involves cleaning, transforming, and structuring the dataset to ensure its usability for subsequent analyses. Once the dataset is prepared, a comprehensive EDA will be performed, which will help us gain some useful insights about the data that we are working with, and the findings will be concluded. The results obtained from this project will serve as a foundation for future studies, urban planning, and transportation optimization. It will offer valuable insights to policymakers, urban planners, and transportation analysts, enabling them to make informed decisions and improve the efficiency and sustainability of urban transportation systems.

Title: Unveiling the Gender Disparity and Diversity of Sex Ratios Across India

Synopsis: The debate on the sex ratio is essential in a diverse country like India for ensuring gender equality, social harmony, and promoting economic and social development. This study focuses on three primary objectives: exploring variations in Sex Ratio at Birth at the Intra-State level, classifying districts in India based on their Sex Ratio at Birth, and assessing the relationship between Sex Ratio Total and Sex Ratio at Birth with socioeconomic variables. The findings reveal significant intra-state variations across all states, with higher variations observed in the Sex Ratio at Birth compared to Sex Ratio Total. District classification identifies certain states where most districts belong to specific clusters, suggesting a relationship between districts regarding Sex Ratio at Birth. Additionally, regional classification through clustering analysis provides a comprehensive understanding of distinct regional characteristics. Analysis of the correlation matrix yields intriguing results regarding the relationship between Sex Ratio at Birth, Sex Ratio Total, and socioeconomic variables.