This project focuses on predicting the price of used cars based on key features such as the car’s make, model, manufacturing year, kilometers driven, and fuel type. By using machine learning models, we aim to build a system that helps estimate the fair market value of a car based on historical data.
Dataset: The dataset contains the following columns:
-->Name: The name or model of the car
-->Company: The manufacturer or brand of the car
-->Year: The manufacturing year of the car
-->Kms Driven: Total kilometers the car has been driven
-->Fuel Type: Type of fuel used (e.g., Petrol, Diesel, CNG, etc.)
-->Price: The price of the car (target variable)
Workflow:
-->Data Cleaning & Preprocessing: Handling missing or inconsistent data (e.g., fixing missing values in 'year' or 'price'). Converting categorical data (like 'fuel_type' and 'company') into numerical representations. Standardizing and normalizing numerical features for better model performance.
-->Exploratory Data Analysis (EDA): Visualizing relationships between features and the target variable. Checking for multicollinearity and feature importance to optimize model inputs.
-->Feature Engineering: Creating new features based on existing ones (e.g., car age derived from ‘year’). Encoding categorical variables and scaling numerical features.
-->Model Building: Training various machine learning models such as:
Linear Regression
Decision Trees
Random Forest
Gradient Boosting
Comparing models to find the best one based on performance metrics.
Model Evaluation: Evaluating models using metrics like:
--> R² Score: To determine how well the model explains the variance.
--> Mean Squared Error (MSE): To measure prediction accuracy.
Hyperparameter tuning to improve performance.
Results & Insights
-->Insights into how factors like car age, brand, and kilometers driven affect the resale price.
-->Identification of the most influential features for price prediction.
Technologies Used:
-->Programming Language: Python
-->Libraries:
##Pandas: For data manipulation and cleaning
##NumPy: For numerical computations
##Scikit-learn: For implementing machine learning algorithms and model evaluation
##Matplotlib & Seaborn: For data visualization and exploratory data analysis
Installation:
-->Clone the repository: git clone https://github.com/yourusername/Car_Price_Prediction.git
-->Navigate to the project directory: cd Car_Price_Prediction
-->Install the required packages: pip install -r requirements.txt
** Usage**
Load the dataset and run the notebook (Car_Price_Prediction.ipynb) to execute the code for data preprocessing, model training, and evaluation.
-->Explore different models and their performance metrics to understand which model works best for predicting car prices.
Future Improvements
Adding more features like car condition, transmission type, or owner history for even more accurate predictions. Testing more advanced models like Neural Networks or XGBoost.
Conclusion:
This project demonstrates the application of machine learning techniques for price prediction in the used car market. It showcases the entire process from data cleaning and EDA to model building and evaluation. The resulting model can help individuals estimate the fair price of a used car based on historical data.