shreekeshavan/car_price_prediction

🚗 Engineered a high-performing car price prediction model, empowering informed decisions by Identifying the key features that most influence car prices in the market. 🚘💰

Car Price Prediction App

Project Description

Welcome to the Car Price Prediction App! This project focuses on developing a state-of-the-art predictive model that accurately estimates car prices based on a wide range of features. By applying skills in data analysis, visualization, machine learning, and programming, this project addresses real-world challenges in the automotive market.

Table of Contents

• Project Description
• Key Points
• Results
• Usage
• Contributions

Key Points

1. Understanding the Data: Gained a solid grasp of the dataset's structure, variables, and relationships, laying the foundation for insightful analysis.

2. Data Cleanup and Enhancement: Meticulously cleaned the data to enhance its usability, effectively managing missing values and inconsistencies.

3. Exploring Data Patterns: Conducted thorough exploratory data analysis (EDA) to uncover hidden patterns, reveal relationships, and identify crucial variables.

4. Enhancing Features: Revamped the model's features using innovative techniques, boosting its ability to make accurate predictions.

5. Data Preparation: Streamlined data processing by encoding categorical variables, scaling numerical features, and dividing the dataset into training and testing sets.

6. Powerful Model Ensemble: Utilized a diverse array of machine learning algorithms, including Linear Regression, Random Forest Regression, Gradient Boosting Regression, Adaboost Regression, and Decision Tree Regression, to create a robust car price prediction model.

7. Fine-Tuning for Performance: Optimized model performance through advanced hyperparameter tuning, leveraging techniques like randomized search.

8. Model Effectiveness Evaluation: Achieved an impressive 95% R2 score using the Random Forest Regression model, meticulously measuring model accuracy and reliability.

9. Identifying Influential Factors: Analyzed feature importance to uncover the key elements driving car price fluctuations, enhancing interpretation and decision-making.

10. Future-Ready Predictive Insights: Armed with a high-accuracy model, confidently predict car prices, bridging the gap between car buyers and sellers in the dynamic automotive market.

Results

Embark on an enriching journey with this project, yielding a sophisticated car price prediction model boasting an exceptional 95% R2 score. Witness the app's prowess in efficiently estimating used car prices based on their features, delivering valuable insights to a discerning audience of automotive enthusiasts and stakeholders.

Usage

1. Run the ipynb file: car_price_prediction_app.ipynb
2. Follow the prompts to explore car price predictions based on various features.

Contributions

Contributions and suggestions are welcomed! Feel free to open an issue or pull request to provide feedback or enhancements.