CAR SALES FORECASTING USING PRICE AND FUEL TYPE ANALYSIS & PREDICTION

Abstract

The Car Sales Forecasting Using Price and Fuel Type Analysis and Prediction system is a data-driven application designed to analyze historical car sales information and predict future sales patterns. The automobile market contains large amounts of information related to car prices, fuel types, brands, models, sales quantities, and customer preferences. Analyzing these factors can help understand market trends and support better planning and decision-making. The proposed system collects historical car sales data containing attributes such as car model, manufacturer, selling price, fuel type, transmission type, year, location, and sales volume. Data preprocessing techniques are applied to clean the dataset, handle missing values, remove duplicate records, and standardize numerical and categorical attributes. The processed data is then used for exploratory analysis and prediction. The system analyzes the relationship between car prices, fuel types, and sales performance. It can compare the sales of petrol, diesel, electric, hybrid, and other fuel categories and examine how price variations influence customer demand. Data visualization techniques such as bar charts, line graphs, scatter plots, and comparison charts are used to present these patterns clearly. A forecasting module uses statistical or Machine Learning techniques to estimate future car sales based on historical patterns and selected input features. The prediction model can be trained using historical sales records and evaluated using suitable performance metrics. Users can enter relevant car attributes and obtain an estimated sales prediction from the trained model. Overall, the proposed system combines data analysis, visualization, and Machine Learning-based forecasting to provide useful insights into the automobile sales market. It can support dealers, manufacturers, analysts, and business managers in understanding price and fuel-type trends. Future enhancements can include real-time market data, advanced forecasting models, customer segmentation, regional demand prediction, electric-vehicle trend analysis, and interactive business intelligence dashboards.