The document outlines a machine learning model designed to predict used car prices based on various specifications, using a dataset from online listings. It employs a random forest algorithm for high accuracy, with evaluation metrics including mean absolute error and R². The model features a user-friendly interface for inputting car details and aims to enhance accuracy and expand functionalities in the future.

1. Car Price Prediction Model
• A Machine Learning Model for Predicting Used Car Prices
• Presented by: [Your Name]
• Date: [Today's Date]

2. Introduction & Objective
• Objective: Build a machine learning model to predict the resale value of used cars based on specifications.
• Problem Statement: Accurately estimating car resale prices to aid buyers and sellers in decision-making.

3. Data & Features
• Data Source: Dataset collected from online car listings (e.g., Kaggle) with thousands of records.
• Key Features:
• - Present Price
• - Kms Driven
• - Fuel Type
• - Seller Type
• - Transmission
• - Owner
• - Age

4. Model Selection & Evaluation
• Algorithm Chosen: Random Forest (chosen for high accuracy and robustness).
• Evaluation Metrics: Mean Absolute Error (MAE) and R².
• Result: Achieved high prediction accuracy with a score of X%.

5. User Interface (GUI)
• Developed a user-friendly interface to input car details and get the predicted price.
• Screenshot of GUI included in presentation to demonstrate layout and usability.

6. Conclusion & Future Scope
• Summary: Successfully created an accessible car price predictor for potential buyers and sellers.
• Future Scope:
• - Improve accuracy
• - Expand features
• - Deploy as a web application