Skip to content

Predict the vehicle price from the open source Auto data set using linear regression. In this data set, we have prices for 205 automobiles, along with other features such as fuel type, engine type,engine size,etc.

Notifications You must be signed in to change notification settings

nafisa-samia/Automobile-Price-Prediction-using-Linear-Regression

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

26 Commits
 
 
 
 
 
 

Repository files navigation

Automobile-Price-Prediction-using-Linear-Regression

Libraries Used: Pandas, scikit-learn, matplotlib

Aim: Our goal is to predict the vehicle price using the open source Auto data set from the UCI machine learning repository. In this data set, we have prices for 205 automobiles, along with other features such as fuel type, engine type,engine size,etc.

Description:

  • We took the dataset from UCI Machine Learning repository, to predict vehicle price using linear regression
  • After handling missing values with different types of imputation, we did some analysis on the dataset
  • Visualized some important features and also analyzed them with their patterns
  • Then we checked linear regression assumptions for the dataset, applied Anderson-Darling test, Goldfeld-Quandt test, etc to check the assumptions
  • When we found multicolinearity in the dataset, we removed 2 columns to overcome multicolinearity problem
  • Then we applied feature engineering pipeline on the rest of the dataset
  • We also tested different feature selections technique and checked model accuracy
  • Finally, we applied regularization and solved model overfitting problem

What we have learned so far from this project:

  • How to handle missing values
  • How to check for linear regression assumptions
  • How to apply feature engineering pipeline
  • Different types of feature selection tools
  • Regularization technique for linear regression

About

Predict the vehicle price from the open source Auto data set using linear regression. In this data set, we have prices for 205 automobiles, along with other features such as fuel type, engine type,engine size,etc.

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published