lection is important because it allows the electorate to decide who's going to make decision for their country for the next couple of years. But this election can be forecasted with a reasonable accuracy. Forecasting election using small polling system is very common approach but this often do not produce reasonable accuracy.

Data mining is a process that examines large preexisting databases in order to generate new information. There are also various works that uses data mining approaches to predict various types of results such as weather forecasting, sports result prediction, future buying decision prediction, etc. But there are very few works that uses data mining approaches to predict voting patterns on election. In this work, we uses data mining approaches to predict voting patterns in USA election. For this study we uses data preprocessing for removing missing value, identifying best attributes and removing duplicate values. We split the dataset into training datasets and test datasets. Then we applied four algorithms Tree J48, Naïve Bayes Classifier, Trees Random Forest and Rules zero or Classifier for predicting voting patterns and also compares the results of those model and finds the best models from those models.

Gregg R. Murray and Anthony Scime uses data mining approaches to predict individual voting behavior including abstention with the intent of segmenting the electorate in useful and meaningful ways [1]. Gregg R. Murray, Chris Riley, and Anthony Scime, in another study, uses iterative expert data mining to build a likely voter model for presidential election in USA [2]. Bae, Jung-Hwan, Ji-Eun, Song, Min uses Twitter data for predicting trends in South Korea Presidential Election by Text Mining techniques [3]. Tariq Mahmood, TasmiyahIqbal, Farnaz Amin, WaheedaLohanna, Atika Mustafa uses Twitter data to predict 2013 Pakistan Election winner [4].

Data Preprocessing

We used 4 algorithms and 8 models (2 models for each algorithm) to predict the voting pattern in the US election. We then analyse and compare the results of those models and finds the best models with most accuracy. The algorithms which are applied for generating models are given below.

i. Trees J48 ii.

Naive From the above table, the best model was identified based on the value of the parameters accuracy, precision, recall, sensitivity, and specificity. The higher the value of accuracy, precision, recall and (sensitivity> specificity), the higher the rank.

Though there are lot of techniques and methods for predicting voting patterns, data mining is the most efficient and effective methods in this fields. In our study, we clearly found that among various data mining algorithms Trees Random Forest performs the best with 98.17% accuracy. In future, we will expand our research in most recent dataset for validating our findings with recent ones.