Introduction:-Gait is one of the few biometric features that can be measured remotely without physical contact and proximal sensing, which makes it useful in surveillance applications. Such applications play a decisive role in monitoring high security areas including banks, airports, military bases and railway stations. In the real world, there are various factors, significantly affecting human gait including clothes, shoes, carrying objects, walking surfaces, walking speeds and observed views. A large number of gait recognition methods have been published recently, which can be roughly divided into two categories, model-based methods include "A new view-invariant feature for cross-view gait recognition" and appearance-based method include "Recognizing gaits across views through correlated motion co-clustering". These methods require a preprocessing of foreground/background segmentation (FG/BG) on a gait video, in order to extract shape contours, silhouettes, skeletons, or body joints for further gait analysis. The modelbased methods generally aim to model kinematics of human joints in order to measure physical gait parameters such as trajectories, limb lengths and angular speeds. The appearance-based methods typically analyze gait sequences without explicit modeling of human body structure. These methods have shown their effectiveness on human gait recognition under fixed view. However, they lack a proper methodology to address the problem of view change.

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Customer churn is a major problem affecting large companies, especially in telecommunication field. So the telecom industries have to take the necessary steps to retain their customers, to maintain their market value. So companies are seeking to develop methods that predict potential churned customers. We have to find out the factors that increase customer churn for making necessary actions to reduce churn. In the past, different data mining techniques have been used for predicting the churners. Here the most popular machine learning algorithms used for churn predicting are analysed. The conclusions are stated with the help of suitable tables.

Customer Churn Prediction is a challenging activity for decision makers because most of the time, churn and non-churn customers have similar features. It is one of the major concerns for large companies, especially in the field of telecommunication field. Churn can be considered as a binary classification. The classifiers shows different accuracy levels at different zones of data. In such cases, a correlation can easily be observed in the level of classifier's accuracy and certainty of its prediction. So a mechanism to estimate the classifier’s certainty for different zones within the data is needed so that the expected classifier’s accuracy can be estimated. Here the classifier’s certainty estimation is done using six sigma rule of normal distribution applied on the correlation values of all features in the dataset. Based on this the dataset is grouped into two categories such as (i) data having high certainty, and (ii) data having low certainty. Based on these criteria, classifier accuracy is estimated in the high distance zone. From the different evaluation measures like accuracy, f-measure, precision, recall and Receiving Operating Characteristics (ROC) area, the performance of classifier is evaluated. Then by applying a k fold approach the certainty of the classifier decision is estimated.