Electric Vehicle (EV) Registration Forecasting

This project analyzes historical data on electric vehicle registrations and provides a forecast for future registrations. Using time series analysis techniques, including SARIMA and Holt-Winters, this project aims to estimate EV adoption trends based on existing data.

Project Structure

• Data Loading and Preprocessing: Load, clean, and prepare EV registration data for analysis.
• Exploratory Data Analysis (EDA): Visualize key trends, distributions, and geographical insights.
• Time Series Analysis and Forecasting: Analyze trends over time and forecast future registrations using SARIMA and other models.

Features

1. Data Cleaning: Handles missing values, duplicates, and filters data by completed years up to 2023.
2. Exploratory Data Analysis (EDA):
   • EV adoption over time
   • Distribution by electric vehicle type, manufacturer, and model
   • Geographical distribution at county and city levels
3. Time Series Forecasting:
   • SARIMA: Seasonal Autoregressive Integrated Moving Average model to capture seasonality and trends.
   • Alternative Model: Holt-Winters Exponential Smoothing as an option if SARIMA or Prophet is unavailable.

Requirements

Ensure you have the following libraries installed:

pip install pandas matplotlib seaborn statsmodels

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This README covers all main aspects of the project, guiding users through setup, usage, and forecasting steps. Let me know if you’d like additional customization!