This repository contains a Python script for building and evaluating a car price prediction model using machine learning techniques. The model is trained to predict car prices based on various features.
Python (3.6 or higher), Pandas, Matplotlib, Seaborn, Scikit-learn
The given model performs the following steps:
Imports necessary libraries such as Pandas, Matplotlib, Seaborn, and Scikit-learn. Loads a dataset containing car features and prices using Pandas. Explores the dataset through data visualization using Matplotlib and Seaborn. Prepares the data by splitting it into features and target, and then splitting into training and testing sets using Scikit-learn. Normalizes the features using StandardScaler to ensure uniformity. Creates a Linear Regression model using Scikit-learn. Fits the model to the training data. Makes predictions on the test data and evaluates the model's performance using various metrics from Scikit-learn.