The data mining classification techniques and analysis can enable banks to move precisely classify consumers into various credit risk group. Knowing what risk group a consumer falls into would allows a bank to fine tune its lending policies by recognizing high risk groups of consumers to whom loans should not be issued, and identifying safer loans that should be issued on terms commensurate with  the risk of default. So research en for classification and prediction of loan grants. The attributes are determined that have greatest effect in the loan grants. For this purpose C4.5, CART and Naïve Bayes are compared and analyzed in this research. This concludes that a bank should not only target the rich customers for granting loan but it should assess the other attributes of a customer as well which play a very important part in credit granting decisions and predicting the loan defaulters.

The augmentation of the populace and the addition of the street have expanded the no of the streetlamp, for the street and individual’s security, which builds the venture and energy. Lighting burns-through satisfactory measure of energy in both open-air and indoor. Anyway, various methodologies are being proposed for making the framework energy effective and updated with the most recent innovation. This exploration gives the exact and the best command over the energy proficient road lighting framework. It will give the functional execution utilizing LoRa and the Arduino just as the data utilizing different sensors. This exploration provides conservation of energy and with efficient monitoring of light as well as accident that happen in road due to less efficacy presences of light.