A popular technique of density estimation is the kernel density estimation (KDE). It is a nonparametric estimation approach which requires a kernel function and a bandwidth (smoothing parameter H). It aid density estimation and pattern recognition. This paper presents new approaches in nonparametric density construction problem, particularly at the boundary points using the dataset and a pilot plot. However, since the main way to improve density estimation is to obtain a reduced mean squared error (MSE). When the MSE for these approaches were evaluated and compared. Some improvements were seen in two proposed approaches. These were achieved under a sufficiently smoothing technique in the existing approaches. These approaches are adaptive and they reduce under fitting and over fitting as the case may be of the data set and aid statistical inference.