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Machine learning plays a major role from past years in image detection, spam reorganization, normal speech command, product recommendation and medical diagnosis. Present machine learning algorithm helps us in enhancing security alerts, ensuring public safety and improve medical enhancements. Machine learning system also provides better customer service and safer automobile systems. In the present paper we discuss about the prediction of future housing prices that is generated by machine learning algorithm. For the selection of prediction methods we compare and explore various prediction methods. We utilize lasso regression as our model because of its adaptable and probabilistic methodology on model selection. Our result exhibit that our approach of the issue need to be successful, and has the ability to process predictions that would be comparative with other house cost prediction models. More over on other hand housing value indices, the advancement of a housing cost prediction that tend to the advancement of real estate policies schemes. This study utilizes machine learning algorithms as a research method that develops housing price prediction models. We create a housing cost prediction model In view of machine learning algorithm models for example, XGBoost, lasso regression and neural system on look at their order precision execution. We in that point recommend a housing cost prediction model to support a house vender or a real estate agent for better information based on the valuation of house. Those examinations exhibit that lasso regression algorithm, in view of accuracy, reliably outperforms alternate models in the execution of housing cost prediction.

Detection and recognition of traffic signs is very important and could potentially be used for driver assistance to reduce accidents and eventually in driverless automobiles.Also traffic signs are essential part of day to day lives. They contain critical information that ensures the safety of all the people . As there are number of traffic signs throughout the world , it is almost impossible for human beings to remember them and identity their meaning which create huge traffic accidents and human loss throughout the world so it is important to establish this project that will remember the traffic signs of all the country throughout the world. Traffic signs classification is the process of identifying which class a traffic sign belongs to. In this project with the help of deep learning , different traffic signs are identified and classified into different categories which helps in reducing various traffic accidents and also reduces human time to remember different traffic signs . In this paper , traffic sign recognition using Convolutional Neural Network(CNN) is implemented ,the CNN will be trained by using GTSRB dataset of 43 different classes containing 50,000 images of traffic signsThe results will show 94% accuracy.

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