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CarDekho Project - Car Price Prediction

Technologies

Description:

This project aims to build a predictive model using car sales data from India, provided by Kaggle. The model will be trained to predict car prices based on various vehicle features.

Goal:

Develop a regression model to predict the selling price of used cars, maximizing the accuracy of predictions.

Data Dictionary:

• name: Car model name.
• year: Year of manufacture.
• selling_price: Car selling price.
• km_driven: Vehicle mileage.
• fuel: Type of fuel used (e.g., petrol, diesel).
• seller_type: Type of seller (individual or dealer).
• transmission: Type of transmission (manual or automatic).
• owner: Number of previous owners.

Libraries Used:

• pandas: For data manipulation.
• seaborn: For data visualization.
• matplotlib: For plotting graphs.
• numpy: For mathematical operations.
• Jupyter lab: For coding environment.

You can install all the required dependencies with the following command:

pip install -r requirements.txt

Project Steps:

1. Data Loading: The dataset was imported and inspected.
2. Exploratory Analysis: Descriptive statistics and visualizations were used to understand the key characteristics of the data.
3. Model Creation: A regression model was trained to predict car selling prices.
4. Model Evaluation: The model was evaluated based on performance metrics to assess its accuracy.

How to Run:

1. Clone the repository:

git clone https://github.com/Gustavo2022003/CarDekho.git

2. Ensure dependencies are installed:

pip install -r requirements.txt

3. Run the Jupyter Notebook: Open the main.ipynb file in a Jupyter environment:

jupyter lab main.ipynb