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Background: As coal is providing major portion of energy requirements and it will continue to provide the same in the Future as well for the next few decades.It describes the information like amount, location, and quality of the coal resources. It is important that we need to use coal in an efficient manner as well as safely.We need to make sure that it is environment friendly so that environment is not effected by the things done by the human.As we can see that coal resources are mostly used in electricity. In this project we are going to estimate the coal production and to predict how much coal we have to use and how much we have to save for future purposes.So,we are using regression algorithms like Lasso and Ridge to estimate the coal production and we are going to compare both algorithms, choose the best one and fine tune the parameters of the algorithm.This project is beneficial for the coal production companies.
We gather and interpret the data in order solve the problem we faced in existing system.
Here, we perform model training and model testing using advanced regression models.
we compare the predicted data with previous data in data comparison.
In this we will see the results and visualize it.
Conclusions: While making a model most important things that we need to remember is feature engineering and selection process.To make the model more robust and accurate make sure to extract the maximum information from the features.The experience and time matters the most in feature selection.There are many ways to deal with our dataset and also many ways to make our model learn. The algorithms we are using should give better results compared to the existing system.For that we can use different algorithms and essemble them or compare all the algorithms and check which one is giving the best and accurate results.So, that it will be easy to choose which algorithm is perfect for our project.That model is used on our project and we can see the accurate results which will be useful for the coal based companies.