2016 Presidential Election Prediction using Twitter

Nowadays, data of social media websites are getting more and more popular to be used as one of the most important data source for the data mining from which we can find the useful and interesting patterns. In this project, base on twitter data set that I collect using twitter API, I performed the sentimental mining and topic modeling. In the data collection phase, I used keywords such as the candidates’ name to filter the related data decreasing the noise to the most extend. To accomplish the sentimental mining, I chose Naïve Bayes algorithm and Support vector machine Model(SVM) two of the most commonly used algorithms that can be used as the classifier in the sentimental analysis. Then I trained these classifiers using a data set which was also from twitter and was related to 2016 presidential election from Kaggle and made the predication using twitter data set that I collected. Besides, Latent Dirichlet Allocation model was used to fulfill the topic modeling analysis finding the most frequent topics from the data of presidential election related tweets. At last, I evaluated the performance of each classification algorithm

Crime Analysis of Chicago

Security status in the place where we are live is one of the most important concerns for every individual. As most people may know, to evaluate the security status, criminal rate cannot be overlooked. Many factors can influence the occurrence of crimes including time, places, the construction of population, educational level, income level and so on. Nowadays, using modern technology such as Hadoop to process large volume of crime dataset, analyze the relationship between crime rate and the related factors and predict the happen of crime are getting more and more popular. In this project, using Hadoop ecosystem and related technologies, we choose crimes dataset of Chicago from 2001 to present to analyze the relationships between the occurrence of crimes and several key factors