(A Research paper (attached above) implementation along with OPTICS and Principal Component Analysis (PCA), and several other pre processing and Exploratory Data Analysis techniques)
Machine learning and big data is an emerging synthetic analysis method which can help process large amounts of data and do analysis on them through programming. In this project, we aim to classify oil wells so that it can be easily managed and also ensure good oil productivity. Firstly preprocessing of the data and adjustment is done. Then we proceed with implementing the two clustering methods namely, K-means and DBSCAN. Lastly, we do analysis on the data, showing the results obtained using these two algorithms, compare the results and use the differences to explore which classification method should be used in the specific occasion.
Lu, C., Shi, Y., Chen, Y., Bao, S. and Tang, L., 2016, November. Data mining applied to oil well using K-Means and DBSCAN. In 2016 7th International Conference on Cloud Computing and Big Data (CCBD) (pp. 37-40). IEEE.