Car_price_ML_model

Car Price Prediction Using Linear Regression

This project aims to predict car prices based on various features such as age, mileage, brand value, and other relevant factors using a linear regression model.

📁 Dataset

The dataset used in this project (car_price.csv) includes the following features:

• Feature 1: [Description of the feature]
• Feature 2: [Description of the feature]
• ...
• Price: The target variable representing the car's price.

The dataset contains N rows and K columns. (Replace N and K with actual numbers.)

Sample Dataset Snapshot:

Feature 1	Feature 2	...	Price
Example 1	Example 2	...	10000

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🔧 Project Workflow

1. Data Preprocessing:

   • Handled missing values.
   • Performed feature scaling (if applicable).
   • Encoded categorical variables.

2. Model Training:

   • Used a linear regression model from sklearn to train on the dataset.
   • Split data into training and testing sets for evaluation.

3. Model Evaluation:

   • Evaluated the model using metrics like Mean Squared Error (MSE) ,MAPE and R² Score.

4. Prediction:

   • The model predicts car prices based on user-provided input values for features.

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🛠️ Installation and Usage

Prerequisites

• Python 3.x
• Libraries: pandas, numpy, sklearn, matplotlib, seaborn

Steps to Run the Project

1. Clone this repository:

   git clone https://github.com/your-username/car_price_ML_model.git
   cd car_price_ML_model