The study will undertake a systematic and rigorous process encompassing data exploration, descriptive analysis, exploratory data analysis (EDA), and multiple linear regression modeling. The initial step involves a thorough exploration of the dataset. This encompasses a meticulous examination of the structure, types, and general characteristics of the variables, setting the stage for subsequent analyses. Following data exploration, the study will conduct a descriptive analysis to unveil fundamental statistics and characteristics of numeric variables. This phase aims to provide a quantitative summary of the dataset, including measures of central tendency, dispersion, and potential outliers, offering a foundational understanding of the distribution and variability within the data. To enhance comprehension and reveal patterns within the dataset, the study will employ data visualization techniques. Figures depicting the frequency distribution of categorical variables, pairwise relationships between numeric variables, and the daily frequency of total rental bikes over time have been included, which will serve as a pivotal component in unraveling trends, relationships, and potential anomalies within the data. Moreover, this phase aims to uncover relationships between variables, particularly focusing on how categorical predictors may influence the total count of rented bikes.