This project requires you to understand what mode of transport employe This project requires you to understand what mode of transport employees prefers to commute to their office. The attached data 'Cars.csv' includes employee information about their mode of transport as well as their personal and professional details like age, salary, and work experience. We need to predict whether or not an employee will use a car as a mode of transport. Also, which variables are a significant predictor behind this decision? Following is expected from the candidate in this assessment: Perform an exploratory data analysis (EDA) on the data, illustrating insights based on the EDA, checking for multicollinearity, plotting graphs based on multicollinearity, and treating multicollinearity if present.
Paper For Above instruction The aim of this project is to analyze employee transportation preferences with an emphasis on understanding the factors influencing the choice to use a car as a mode of transport. To achieve this, a comprehensive exploratory data analysis (EDA) will be conducted on the dataset, followed by a multicollinearity assessment to refine predictive variables. This report encompasses insights derived from the data, identifies significant predictors, and discusses how multicollinearity is detected and addressed. Introduction Understanding employees' commuting modes is vital for organizations aiming to promote sustainable transportation options and improve workforce management. The dataset provided, 'Cars.csv', offers a mixture of personal demographic information and professional background, along with their chosen mode of transportation. The primary focus is to build a predictive understanding of whether an employee is likely to use a car for commuting, based on these features. A detailed exploratory analysis serves as a fundamental step to visualize data distributions, uncover patterns, and identify potential predictor variables. Moreover, multicollinearity among predictors must be examined because high correlations between independent variables can distort the model's interpretation and performance. Exploratory Data Analysis (EDA) The first step involves importing the dataset and gaining a preliminary understanding of its structure. This includes inspecting variable types, distributions, and missing values. Descriptive statistics provide insight into the central tendency and variability of numerical features like age, salary, and work experience. Visualizations such as histograms and box plots for continuous variables reveal distribution patterns and