This project showcase the powerful integration of MongoDB with data analysis and machine learning workflows.

Objective:
Build a machine learning model to predict car prices based on various features such as make, model, year, mileage, etc., using MongoDB as the backend database.

Key Features:

• Data Preprocessing: Efficiently storing, retrieving, and preprocessing car-related data using MongoDB.
• Model Building: Implementing regression models to predict car prices with high accuracy.
• Model Evaluation: Evaluating model performance using metrics like Mean Absolute Error (MAE) and R-squared.

Outcome:
Achieved a predictive model capable of estimating car prices with a good degree of accuracy, demonstrating the seamless integration of MongoDB with machine learning pipelines.

• Python 3.x
• MongoDB installed locally or accessible via cloud
• Required Python packages listed in requirements.txt

1. Clone the repository:
2. Install the required dependencies:

   pip install -r requirements.txt

3. Ensure MongoDB server instance is running and accessible.

• Navigate to the respective project directories to find detailed instructions on how to run the code.