This project aims to analyze market trends and predict the prices of used cars by leveraging web scraping techniques and regression models. The primary source of data is the Cars24 website, which provides extensive information about used cars available for sale in various cities.
Key Components:
1. Web Scraping:
• Objective: To gather data on used cars from the Cars24 website.
• Tools: BeautifulSoup and Selenium are used to scrape the website.
• Data Collection: Information is scraped for 11 different cities, including details such as car name, type, transmission, kilometers driven, ownership status, fuel type, and pricing details (original and reduced prices).
2. Data Storage:
• DataFrame: The scraped data is organized into a pandas DataFrame for easy manipulation and analysis.
• CSV File: The DataFrame is saved to a CSV file (Assignment_2.csv) for future use.
3. Dataset Description:
• Attributes: The dataset contains various attributes including the car’s name, trim, transmission type, kilometers driven, ownership status, fuel type, EMI per month, original price, reduced price, and the city where the car is located.
• Purpose: These attributes provide a comprehensive overview of each used car, which is essential for conducting a thorough market analysis and for building predictive models.
4. Market Trend Analysis:
• Goal: To identify patterns and trends in the used car market based on the collected data.
• Techniques: Statistical analysis and data visualization techniques are employed to understand the relationships between different variables and their impact on car prices.
5. Price Prediction:
• Regression Models: Various regression techniques are used to predict the prices of used cars.
• Training and Testing: The data is split into training and testing sets to evaluate the performance of the predictive models.
• Evaluation Metrics: Metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) are used to assess the accuracy of the predictions.
Expected Outcomes:
• Market Insights: Gaining insights into the factors affecting used car prices and identifying key trends in the market.
• Predictive Model: Developing a reliable model that can accurately predict the price of a used car based on its attributes.
Applications:
• Consumers: Helping consumers make informed decisions when buying or selling used cars.
• Dealers: Assisting car dealers in pricing their inventory competitively.
• Market Analysis: Providing valuable data-driven insights for market analysts and researchers.