Using going to use Regression Technique of Machine learning to predict the price of Bulldozer based on the given dataset.
In this notebook, we're going to go through an example machine learning project with the goal of predicting the sale price of bulldozers.
Since we're trying to predict a number, this kind of problem is known as a regression problem.
The data and evaluation metric we'll be using (root mean square log error or RMSLE) is from the Kaggle Bluebook for Bulldozers competition.
The techniques used in here have been inspired and adapted from the fast.ai machine learning course.
To work through these topics, we'll use pandas, Matplotlib and NumPy for data anaylsis, as well as, Scikit-Learn for machine learning and modelling tasks.
We'll work through each step and by the end of the notebook, we'll have a trained machine learning model which predicts the sale price of a bulldozer given different characteristics about it.
Please note that this problem we are trying to solve here is there on kaggle hence you can find the dataset to it on the kaggle website. Still I'm providing the link for the same here: https://www.kaggle.com/c/bluebook-for-bulldozers/data
Do go through the colab notebook to see what approach I have adapted to solve this regression problem. I have tried explaining every cell on the way inside the colab notebook, to make it more communicative and informative. Hope people reading this find insightful and informative.
Till then......Happy Learning!!!