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🚗 Car Dheko - Used Car Price Prediction

Accurate and Interactive Tool for Estimating Used Car Prices
Using Data Science, Machine Learning, and Streamlit

Project Banner Project Status Technologies

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.

🌟 Project Overview

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.

🧰 Skills and Tech Stack

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

📑 Dataset

  • 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.

🛠 Project Workflow

1. Data Processing

  • 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).

2. Exploratory Data Analysis (EDA)

  • Visualization: Identify patterns and trends.
  • Feature Selection: Analyze key features impacting car prices.

3. Model Development

  • Algorithms: Train various regression models like Linear Regression, Random Forest, and Gradient Boosting.
  • Hyperparameter Tuning: Use Grid Search for optimal parameters.

4. Model Evaluation and Optimization

  • Metrics: MAE, MSE, and R-Squared.
  • Feature Engineering: Enhance model accuracy with feature adjustments.

5. Deployment

  • Streamlit Application: Provides real-time price prediction based on user input.
  • UI Design: Interactive, easy-to-use interface for customers and sales teams.

🎨 Streamlit Application

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.

📊 Results and Deliverables

  • 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.

💡 Project Insights

  • 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.

📄 License

This project is licensed under the MIT License.

🌐 Connect with Us

For feedback, collaboration, or queries, reach out via:

Built with passion for Car Dheko by Udhaya Kumar V