The EDA involved the following steps:

1. Data Loading and Overview: Initial exploration to understand the structure and contents of the dataset.
2. Data Cleaning: Handling missing values, correcting data types, and removing outliers.
3. Descriptive Statistics: Generating summary statistics to understand the central tendencies and variability of key variables.
4. Visualizations: Creating charts and graphs to visualize distributions, correlations, and trends.

Welcome to the Electric Vehicle Population EDA project! This project aims to explore an extensive dataset on electric vehicles (EVs), focusing on various aspects to understand the dynamics and preferences within the EV market. The analyses cover multiple angles, including vehicle type distribution, economic factors, and pricing strategies.

The project is organized into the following sections:

• Introduction: Overview of the project's objectives and the dataset.
• Analysis Highlights: Key findings from the data analysis.
• Data Preprocessing: Steps taken to clean and preprocess the data.
• Exploratory Data Analysis (EDA): Detailed analysis, including visualizations and statistical summaries.
• Conclusion: Summary of insights and their implications.

• Vehicle Type Distribution: Analysis of the proportions and characteristics of Battery Electric Vehicles (BEVs) and Plug-in Hybrid Electric Vehicles (PHEVs), noting significant differences in market penetration and consumer preferences.
• Impact of Electric Range on Economic Factors: Examination of how the electric range of vehicles affects their eligibility for incentives such as the Clean Alternative Fuel Vehicle (CAFV) program, finding a positive correlation between range and eligibility.
• Relationship Between Vehicle Type and MSRP: Simulation of pricing strategies to understand how BEVs and PHEVs are positioned differently in the market, particularly focusing on their cost implications.

To run this project, you need to have the following software installed:

• Python 3.x
• Jupyter Notebook
• Pandas
• Matplotlib
• Seaborn
• SQLite (optional for database storage)