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Used Cars Price Prediction

AIM

Predicting the prices of used cars based on their configuration and previous usage.

https://www.kaggle.com/datasets/avikasliwal/used-cars-price-prediction

https://www.kaggle.com/code/sid4ds/used-cars-price-prediction/

LIBRARIES NEEDED

LIBRARIES USED
  • pandas
  • numpy
  • scikit-learn (>=1.5.0 required for Target Encoding)
  • xgboost
  • catboost
  • matplotlib
  • seaborn

DESCRIPTION

Why is it necessary?

  • This project aims to predict the prices of used cars listed on an online marketplace based on their features and usage by previous owners. This model can be used by sellers to estimate an approximate price for their cars when they list them on the marketplace. Buyers can use the model to check if the listed price is fair when they decide to buy a used vehicle.
How did you start approaching this project? (Initial thoughts and planning)
  • Researching previous projects and articles related to the problem.
  • Data exploration to understand the features.
  • Identifying different preprocessing strategies for different feature types.
  • Choosing key metrics for the problem - Root Mean Squared Error (for error estimation), R2-Score (for model explainability)
Mention any additional resources used (blogs, books, chapters, articles, research papers, etc.).

EXPLANATION

DETAILS OF THE DIFFERENT FEATURES

Feature Name Description Type Values/Range
Name Car model Categorical Names of car models
Location City where the car is listed for sale Categorical Names of cities
Year Year of original purchase of car Numerical Years (e.g., 2010, 2015, etc.)
Kilometers_Driven Odometer reading of the car Numerical Measured in kilometers
Fuel_Type Fuel type of the car Categorical [Petrol, Diesel, CNG, Electric, etc.]
Transmission Transmission type of the car Categorical [Automatic, Manual]
Owner_Type Number of previous owners of the car Numerical Whole numbers
Mileage Current mileage provided by the car Numerical Measured in km/l or equivalent
Engine Engine capacity of the car Numerical Measured in CC (Cubic Centimeters)
Power Engine power output of the car Numerical Measured in BHP (Brake Horsepower)
Seats Seating capacity of the car Numerical Whole numbers
New_Price Original price of the car at the time of purchase Numerical Measured in currency

WHAT I HAVE DONE

Exploratory Data Analysis

  • Summary statistics
  • Data visualization for numerical feature distributions
  • Target splits for categorical features

Data cleaning and Preprocessing

  • Removing rare categories of brands
  • Removing outliers for numerical features and target
  • Categorical feature encoding for low-cardinality features
  • Target encoding for high-cardinality categorical features (in model pipeline)

Feature engineering and selection

  • Extracting brand name from model name for a lower-cardinality feature.
  • Converting categorical Owner_Type to numerical Num_Previous_Owners.
  • Feature selection based on model-based feature importances and statistical tests.

Modeling

  • Holdout dataset created for model testing
  • Setting up a framework for easier testing of multiple models.
  • Models trained: LLinear Regression, K-Nearest Neighbors, Decision Tree, Random Forest, AdaBoost, Multi-Layer Perceptron, XGBoost and CatBoost.
  • Models were ensembled using Simple and Weighted averaging.

Result analysis

  • Predictions made on holdout test set
  • Models compared based on chosen metrics: RMSE and R2-Score.
  • Visualized predicted prices vs actual prices to analyze errors.

PROJECT TRADE-OFFS AND SOLUTIONS

Training time & Model complexity vs Reducing error

  • Solution: Limiting depth and number of estimators for tree-based models. Overfitting detection and early stopping mechanism for neural network training.

SCREENSHOTS

Project workflow

  graph LR
    A[Start] --> B{Error?};
    B -->|Yes| C[Hmm...];
    C --> D[Debug];
    D --> B;
    B ---->|No| E[Yay!];
Data Exploration

target_dist

featdist_year

featdist_kmdriven

featdist_engine

featdist_power

featdist_mileage

featdist_seats

Feature Selection

featselect_corrfeatures

featselect_corrtarget

featselect_mutualinfo

MODELS USED AND THEIR PERFORMANCE

Model RMSE R2-Score
Linear Regression 3.5803 0.7915
K-Nearest Neighbors 2.8261 0.8701
Decision Tree 2.6790 0.8833
Random Forest 2.4619 0.9014
AdaBoost 2.3629 0.9092
Multi-layer Perceptron 2.6255 0.8879
XGBoost w/o preprocessing 2.1649 0.9238
XGBoost with preprocessing 2.0987 0.9284
CatBoost w/o preprocessing 2.1734 0.9232
Simple average ensemble 2.2804 0.9154
Weighted average ensemble 2.1296 0.9262

CONCLUSION

WHAT YOU HAVE LEARNED

Insights gained from the data

  1. Features related to car configuration such as Power, Engine and Transmission are some of the most informative features. Usage-related features such as Year and current Mileage are also important.
  2. Seating capacity and Number of previous owners had relatively less predictive power. However, none of the features were candidates for removal.
Improvements in understanding machine learning concepts
  1. Implemented target-encoding for high-cardinality categorical features.
  2. Designed pipelines to avoid data leakage.
  3. Ensembling models using prediction averaging.
Challenges faced and how they were overcome
  1. Handling mixed feature types in preprocessing pipelines.
  2. Regularization and overfitting detection to reduce training time while maintaining performance.

USE CASES OF THIS MODEL

  • Sellers can use the model to estimate an approximate price for their cars when they list them on the marketplace.
  • Buyers can use the model to check if the listed price is fair when they decide to buy a used vehicle.

FEATURES PLANNED BUT NOT IMPLEMENTED

  • Complex model-ensembling through stacking or hill-climbing was not implemented due to significantly longer training time.