The objective of the project is to analyze deaths that involves Tesla cars data to gain insights and understand the most notable causes for that deaths. The dataset used in this project includes information about Tesla accidents such as: Case #, Year, Date, Country, State, Description and Deaths, among others.
The project aims to evaluate Tesla’s autopilot involvement in crashes reported by Tesla Owners and the safety of the vehicle itself. The analysis also looks into driver and copilot safety in comparison to third parties involved (pedestrians, other cars, motorcycles etc)
Dataset The dataset used for this analysis project consists of a collection of Tesla crash records. It includes various attributes related to each crash, such as:
Case # Year Date Country State Description Deaths Tesla driver Tesla occupant Other vehicle Cyclists/ Peds TSLA+cycl / peds Model AutoPilot claimed Verified Tesla Autopilot Death Source Note Deceased
The dataset has been obtained from reliable sources, including official accident reports, insurance databases, and public records. It is important to note that all personal and sensitive information has been anonymized and removed to protect the privacy of individuals involved in the crashes. The dataset was obtained from Kaggle.
Methodology The data analysis project follows a structured approach:
Data Cleaning: The dataset was reviewed and outliers and irrelevant data points were either removed or appropriately handled.
Exploratory Data Analysis (EDA): First, an analysis was conducted to identify the characteristics of the dataset, and then visualizations, summary statistics, and relationships between different variables were included.
Statistical Analysis: We used Hypothesis testing, regression analysis, and correlation analysis to draw meaningful conclusions.
Visualization: Visual representations, such as graphs, charts, and maps, will be utilized to present the findings of the analysis in a clear and concise manner.
Tools and Technologies The following tools and technologies will be used in this data analysis project:
Programming Language: Python Data Manipulation and Analysis Libraries: Pandas, NumPy Data Visualization Libraries: Matplotlib, Seaborn Statistical Analysis Libraries: SciPy, StatsModels Integrated Development Environment (IDE): Jupyter Notebook or any other preferred IDE Machine Learning Libraries (if applicable): Scikit-learn, TensorFlow, or PyTorch (for advanced analysis or predictive modeling)
Conclusion This README file provides an overview of the Tesla Crash Data Analysis Project to analyze the initial hypothesis.