Accurate and Interactive Tool for Estimating Used Car Prices
Using Data Science, Machine Learning, and Streamlit
This project enhances Car Dheko's customer experience by deploying a streamlined ML model to predict used car prices accurately. Leveraging a dataset with historical prices across multiple cities, we perform data cleaning, feature engineering, and model optimization to deliver reliable predictions. The final model is deployed as a user-friendly Streamlit application, allowing users to get real-time price estimates with ease.
Objective:
Transform customer interactions and streamline pricing decisions by building a machine learning model that predicts used car prices based on detailed car features.
Scope:
Analyze data from multiple cities with features such as car make, model, year, mileage, fuel type, and transmission. The end goal is to deploy a tool that predicts prices accurately based on these attributes and is accessible to both customers and sales representatives through an interactive web app.
| Skill Area | Description |
|---|---|
| Data Cleaning & Preprocessing | Handling missing values, scaling, and encoding |
| Exploratory Data Analysis (EDA) | Understanding data distribution and feature importance |
| Machine Learning | Model development, training, tuning |
| Model Evaluation | Comparing MAE, MSE, R-Squared metrics |
| Streamlit Application | Deploying the model in a user-friendly interface |
| Documentation | Comprehensive reporting and project summary |
- Source: Car Dheko data, spanning multiple cities with features like make, model, year, fuel type, transmission type, etc.
- Structure: Structured data format with columns representing car features and target prices.
- Concatenation: Combine multiple city datasets into one structured dataset.
- Missing Value Handling: Use imputation methods for both numerical and categorical data.
- Standardization: Normalize and clean data (e.g., converting units, handling categorical values).
- Visualization: Identify patterns and trends.
- Feature Selection: Analyze key features impacting car prices.
- Algorithms: Train various regression models like Linear Regression, Random Forest, and Gradient Boosting.
- Hyperparameter Tuning: Use Grid Search for optimal parameters.
- Metrics: MAE, MSE, and R-Squared.
- Feature Engineering: Enhance model accuracy with feature adjustments.
- Streamlit Application: Provides real-time price prediction based on user input.
- UI Design: Interactive, easy-to-use interface for customers and sales teams.
The app features:
- Simple User Inputs: Enter car details like make, model, year, etc.
- Instant Prediction: Real-time price predictions.
- User-Friendly Design: Intuitive and responsive interface.
- ML Model: Accurate prediction model with high performance on test data.
- Interactive App: User-friendly tool for estimating car prices.
- Documentation: Clear explanation of methodology, data processing steps, and results.
- Leveraging features like car age, mileage, and condition significantly impacted model performance.
- Data preprocessing played a key role in ensuring model accuracy by handling missing values and standardizing features.
- The Streamlit app allows users to make predictions effortlessly, improving usability and enhancing customer engagement.
This project is licensed under the MIT License.
For feedback, collaboration, or queries, reach out via:
Built with passion for Car Dheko by Udhaya Kumar V