Revving Up Predictions: Forecasting Car Sales Trends in the UK

Welcome to the repository for the project "Revving Up Predictions," where we explore and forecast car sales trends in the United Kingdom.

Overview

This project aims to leverage time series forecasting techniques to predict car sales trends in the UK. By analyzing historical data and using advanced forecasting models, we aim to provide insights into future car sales, helping stakeholders make informed decisions.

Business Domain

The automotive industry is a critical sector in the UK, contributing significantly to the economy and serving as a key indicator of consumer confidence. Accurate predictions of car sales trends play a crucial role for manufacturers, dealerships, and policymakers. Insights gained from this forecasting project can help stakeholders make informed decisions regarding inventory management, marketing strategies, and economic planning.

Problem Statement

The goal of this project is to develop robust time series forecasting models capable of predicting car sales trends in the UK. By leveraging historical sales data, we aim to provide actionable insights to industry professionals who depend on accurate forecasts for strategic decision-making.

Project Objectives

1. Accurate Forecasting: Develop models that accurately predict future car sales, enabling stakeholders to anticipate market trends.
2. Key Influencing Factors: Identify and analyze key factors influencing car sales, such as economic indicators, seasonal patterns, and external events.
3. Decision Support: Provide a tool that serves as a decision support system for manufacturers and dealerships, aiding in inventory planning and marketing strategies.

Project Structure

• data: Contains datasets related to car sales in the UK. Dataset From 1.link, 2.Kaggle
• notebooks: Jupyter notebooks with data exploration, model training, and evaluation.
• models: Saved models or model-related files.
• results: Visualizations and results generated during the analysis.

Methodology

1. Data Preprocessing:

   • Exploratory Data Analysis (EDA) to understand the dataset.
   • Handling missing values, outliers, and other preprocessing steps.

2. Model Selection:

   • Comparison of various time series forecasting models, including ARIMA, SARIMA, and Prophet.
   • Selection of the best-performing model based on evaluation metrics.

3. Training and Evaluation:

   • Model training on historical data.
   • Evaluation using metrics such as Root Mean Squared Error (RMSE).

4. Results and Insights:

   • Visualization of predicted vs actual car sales.
   • Insights into key factors influencing car sales trends.

How to Use

1. Clone the Repository:

   git clone https://github.com/raghavendranhp/Forecasting-Car-Sales-Trends.git

2. Navigate to the Project Directory:

   cd Sources Sales_table

3. Install Dependencies:

   pip install -r requirements.txt

4. Explore the Notebooks:

   • Open Jupyter notebooks in the notebooks directory to follow the analysis and modeling process.

5. Run Your Own Analysis:

   • Modify or extend the code to suit your specific use case.

Results

Include visualizations, key findings, and any noteworthy insights from your analysis.

Future Work

Highlight potential areas for further improvement, such as exploring additional features or refining models.

Contributors

• Raghavendran S
• LinkedIN

Feel free to contribute by opening issues or submitting pull requests.

License

This project is licensed under the MIT License.