This project applies linear regression to a real-world dataset, aiming to demystify the process and demonstrate the practical application of linear regression in extracting meaningful insights from data.
Linear regression is a statistical method used to identify and quantify the linear relationship between two variables. This method aims to establish a straight line that best fits the given data points, connecting them based on a set of assumptions. It assumes that the data is free of noise, allowing clear identification of true trends. By employing linear regression, we seek to understand the extent to which changes in one variable correlate with changes in another.
In the context of predicting secondhand car prices, linear regression helps establish a relationship between the predictor variable (e.g., features of the car) and the response variable (i.e., the secondhand price). The resulting equation from simple linear regression can be used to model and predict the dependent variable based on the independent variable, providing insights into the relationship between these two factors.
Before running the project, install the required libraries:
pip install numpy pandas statsmodels matplotlib seaborn scikit-learnUsage To run this project:
Clone the repo:
git clone https://github.com/anandsullad777/Linear-regreesion-on-predicting-secondhand-price-for-cars.gitData is collected from the kaggle website
Distributed under the MIT License. See LICENSE for more information.