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The development of smart cities has occurred over the past ten years. One primary goal of “smart city” initiatives is to lessen vehicle congestion. Several innovative technologies, including vehicular communications, navigation, and traffic control, have been created by Vehicle Networking System to address this problem. The traffic data gathered by smart devices aids in the forecasting of traffic in smart cities. This project created an Intelligent Traffic Congestion Management System (ITCMS) that uses machine learning techniques and traffic data from Kaggle to decrease the amount of time spent stuck in traffic. This study aims to assess feature selection methods and machine learning models for traffic forecasting in smart cities. The feature dimension is reduced using feature selection techniques, such information gain, correlation attribute, and principal component analysis. The recommended model successfully predicted traffic flow, assisting in the alleviation of congestion. The principal component analysis with random forest model outperforms the other machine learning models and has a 95% accuracy rate.

Big data analysis is predicated on large amount of data. Diabetes is caused due to the excessive amount of sugar condensed into the blood. One of the most critical chronic healthcare problems is diabetes.Undiagonosed diabetes problem may leads to damage eyes, heart, kidneys and nerves of diabetes patients. If improper medication taken is done which also lead to death. Early detection of diabetes is very important to maintain healthy life. Machine learning algorithm to identify a best predicting algorithm based various matrices such us accuracy, precision, recall, F-measure, sensitivity and specificity. This paper discusses about various ML techniques to predict the Diabetes disease by using dataset .Machine learning algorithm namely Decision Tree, SVM, Naive Bayes, Random forest, k-NN, K-mean clustering and LR algorithms are used in their experiment to detect diabetes at an early stage. Experiments are performed on Pima Indian diabetes dataset (PIDD) which is sourced from UCI Machine Learning repository. Result obtained show SVM outperform with high accuracy of 82% comparatively other algorithms.

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