/oibsip_taskno_3

This is the repository for car price prediction using machine learning models

Primary LanguageJupyter Notebook

Car price prediction

Here the task is to train a machine learning model that can learn from the different factor of car and predict the selling price.

workflow :

  1. Data Acquisition: The car feature & price dataset consist 301 samples with 8 features and 1 target variable(price).

  2. Data preprocessing and EDA : This is the crucial step in any machine learning project. Here handling the duplicate values, converting categorical variable into numerical, visulizing the data for analysis using python libraries and checking correlation between features.

  3. Splitting the data and model training : model that are used here :
    Linear regression
    Random forest regression
    Ridge regression
    Lasso regression
    Elastic net regression
    Gradient boosting regression
    XGBoost regression(best fit)

  4. Result : Comparing the final result and saving the best fit model. Here the XGBoost regression gives the highest R2 score : 0.9380