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Here’s the English translation of the content:

CarDekho Project - Car Price Prediction

Technologies

Jupyter Notebook Python Matplotlib NumPy Pandas scikit-learn

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
  1. Ensure dependencies are installed:
pip install -r requirements.txt
  1. Run the Jupyter Notebook: Open the main.ipynb file in a Jupyter environment:
jupyter lab main.ipynb