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INTRODUCTION

Over its lifetime an average fuel-based vehicle produces around 16 to 32 metric tons of GHG and consumes 6000 to 7500 gallons of gasoline, which an electronic vehicle strives to reduce. In a span of 2-3 years, an EV might reduce greenhouse gas emissions by approximately 2 to 4 metric tons and save 750 to 1,000 gallons of gasoline that would have been consumed by a conventional car. Purpose of Analysis: It delves into the veracity of the model that companies or suppliers use while distributing the rebate amount that is based on the battery range. Objective of the Project: To find best model to determine EV efficiency and promote better decision for customers and manufacturers.

DATA COLLECTION, MINING TECHNIQUES AND TOOLS

Data Source: Obtained from https://data.world/. Data Mining Techniques: Clustering: Utilized K-prototype and K-means for unsupervised learning. Regression Analysis: Employed for supervised learning.
Descriptive Analysis: Extracted patterns and trends related to EV types and efficiency. Software/Tools Used: Excel, Python, Google Colab: Utilized for data analysis and processing.

DATA MINING THROUGH

  • Descriptive analysis
  • Cluster Analysis: K-Prototype
  • REBATE ATTRIBUTION & EFFICIENCY ANALYSIS-Rebate Distribution Discrepancy: Cluster analysis suggests less attribution of rebate amounts to certain models, despite their efficiency in reducing GHG and Petroleum. Seeking Enhanced Predictors: Moving forward, our focus is on identifying superior predictors to measure EV efficiency, aiming to align them with the rebate amounts distributed by suppliers.
  • Cluster analysis: K-Means (EV_Type)
  • Cluster analysis: K-Means (Manufacturer)
  • LINEAR REGRESSION MODELING

KEY FINDINGS AND IMPLICATIONS

Identified Gaps in EV Efficiency Measurement: Discovered crucial gaps in evaluating EV efficiency for suppliers and manufacturers in rebate distribution, influencing customer adoption. Battery Range as a Limited Predictor: Revealed limitations in using battery range as a sole parameter for correlating with GHG and petroleum reduction. Efficiency Comparison: Efficiency of Battery-Powered EVs over Plug-in models. Performance Insights on Manufacturers:
Leading the Promise: Toyota, Tesla, Honda are aligning with industry promises.
Underperformance Noted: Kira, Mitsubishi, Jaguar, while surpassing conventional fuel-based vehicles, fall short in delivering the promised value in the EV market.

CONCLUSION

Impact of the Project: This analysis offers stakeholders an opportunity to reassess the EV market segment's value, prompting informed decision-making and dispelling misinformed beliefs.