Data Science | Moneyball Project ⚾️
Developed an immersive Data Visualization project inspired by the iconic movie "MONEYBALL," the project delves into decades of Major League Baseball (MLB) salaries and statistics. Leveraging R, I conducted a thorough analysis, implementing advanced data manipulation, cleaning, and visualization techniques.
Tools Utilized:
R: The backbone of the project, facilitating robust statistical analysis and data manipulation.
Dplyr and Tidyr: Essential R packages employed for efficient data wrangling, ensuring a clean and organized dataset.
ggplot: A powerful package for creating captivating and informative visualizations, enhancing the understanding of complex data relationships.
Plot.ly: Implemented for interactive and responsive data visualization, enabling users to engage with the insights seamlessly.
Machine Learning | Linear Regression Project 🤖
Took on a project predicting how many bikes get rented each hour using data from two years. The goal was to guess the count before people actually rented bikes. Used ggplot and caTools to make accurate predictions and create easy-to-understand charts. The project aimed to show when and why people rent bikes a lot.
Tools Utilized:
R: Served as the backbone of the project, facilitating robust statistical analysis and data manipulation.
ggplot: Employed this powerful R package for crafting visually compelling and informative plots, illustrating the distribution of bike rentals over time.
caTools: Integrated caTools, a suite of utility functions for classification and regression tasks, to streamline the predictive modeling process, contributing to the efficiency and reliability of total count predictions.
World Economy | Data Visualization Project 🌍
Embarked on a data visualization endeavor using ggplot2, the project aimed to delve into the relationship between the Corruption Perceptions Index (CPI) and the Human Development Index (HDI) across various countries. Leveraging the R programming language and the data.table library, an extensive dataset ('Economist_Assignment_Data.csv') was loaded for analysis.
Tools Utilized:
R: Served as the primary programming language, providing the foundation for statistical analysis, data manipulation, and visualization.
ggplot2: Utilized for creating clear and visually compelling plots, facilitating the exploration of the relationship between the Corruption Perceptions Index (CPI) and the Human Development Index (HDI).
data.table: Employed for efficient and streamlined data reading, enabling the creation and manipulation of a data frame from the 'Economist_Assignment_Data.csv' file.