ELECTRIC CAR REGISTRATIONS & RENEWABLE ENERGY PRODUCTION

RoleData Analysis
Year2026

Project Details

ELECTRIC CARREGISTRATIONS &RENEWABLE ENERGY PRODUCTION NO CONDUCT A DETAILED ANALYSIS OF A CHOSEN DATASET, UNCOVERING INSIGHTS, TRENDS, AND PATTERNS THROUGH DATA PREPROCESSING, EXPLORATORY DATA ANALYSIS (EDA), MODELING, AND VISUALIZATION.

Skills

Data AnalysisData AnalyticsData Visualization

Tools

ExcelPythonPower BI

1. Cleaning the Data

Before analysis, the messy parts of the datasets were removed:

Duplicate rows were dropped.

Extra or irrelevant entries were deleted.

Columns were renamed to make them easier to understand (e.g., “Registration Count” → “REGISTRATION”).

2. Exploratory Data Analysis (EDA)

This is the “getting to know the data” step:

Histograms (bar charts) were made to show:

Bar charts compared:

Heatmaps showed correlations (relationships) between different vehicle categories.

3. Key Visualizations

Top 5 states by private vehicle ownership (California, Texas, Florida, etc.).

Top 5 states by public bus ownership.

Energy generation by source (coal, wind, solar, etc.).

Energy generation by producer type (utilities vs. independent producers).

EV registrations mapped across states using geospatial visualization (a U.S. map shaded by EV counts).

4. Correlation Analysis

This part looked at the relationship between EV adoption and energy sources:

EV registrations were strongly correlated with clean energy sources:

EV registrations had negative correlation with coal (–0.09), meaning states relying on coal had fewer EVs.

(Correlation here means “how closely two things move together.” A value near 1 = strong positive link, near –1 = strong negative link.)

5. Time Series Analysis

A line chart showed total energy generation over time (1990–2019).

Clean energy (solar, wind, hydro) grew steadily.

Fossil fuels (coal, petroleum) declined.

6. Policy Impact Assessment

The study compared states with strong EV/clean energy policies vs. those without.

A t-test (a statistical test) showed no significant difference between policy vs. non-policy states in terms of clean energy share.

7. Main Findings

Private vs. Public Adoption

Public fleets are smaller but cleaner

Policy effectiveness

Energy trends

8. Conclusion

Individuals (private owners) are the main drivers of EV adoption.

Public fleets, though smaller, are more aligned with clean energy.

Policies matter more over time than across states.

The overall trend is clear: EV adoption grows alongside clean energy expansion.

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