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Whatsapp has been the most used mode of communication and has been an efficient one too. It consists of many conversations in groups and individuals. So, there might be some hidden facts in them. This project takes those chats and provide a deep analysis of that data. Being any topic, the chats are it provide the analysis in an efficient and accurate way. The main advantage of this project is that it has been built using libraries like pandas, seaborn, matplotlib, emoji etc. They are used to create data frames and plot graphs in an efficient way.

Data compression plays a vital role in data security as it saves memory, transfer speed is high, easy to handle and secure. Mainly the compression techniques are categorized into two types. They are lossless, lossy data compression. The data format will be an audio, image, text or video. The main objective is to save memory of using these techniques is to save memory and to preserve data confidentiality, integrity. In this paper, a hybrid approach was proposed which combines Quotient Value Difference (QVD) with Huffman coding. These two methods are more efficient, simple to implement and provides better security to the data. The secret message is encoded using Huffman coding, while the cover image is compressed using QVD. Then the encoded data is embedded into cover image and transferred over the network to receiver. At the receiver end, the data is decompressed to obtain original message. The proposed method shows high level performance when compared to other existing methods with better quality and minimum error.

Mobile cloud computing (MCC) is a technology which provides cloud server resources to mobile users with optimized latency. MCC allows mobile device to access cloud resources and to offload tasks to cloud let servers at any time and from anywhere. The cloud let servers are attached to wireless Access points. But mobility plays an important role which leads to the loss of connectivity of mobile devices because of varying signal strengths. On the other hand, optimal code execution is a challenge. In this paper, a connectivity base mobility prediction method is proposed to assign the cloud resources to the user without loss in the connection. The past accessing history of the users and path loss factors are taken into consideration to predict proper access point. From the performance evaluation the performance of the proposed method is increased by 15.23% when compared to other existing methods.