Analysis model through data visualization, and data anlysis

The random forest has no good validation strategies. Therefore it was necessary to visualize the data with a different approach.

[x] Import Library Seaborn
[x] Univariate distributions
[x] Histogram aims to approximate the underlying probability density function
[x] Bivariate Distribution
[x] Analysis of bivariate distribution through KDE plot smoothes with a 2D Gaussian. By default, represent the 2D density by plotting more contours.
[x] Max_tib_lat as X, Max_tib_Med as Y to generate the bivariate distribution, such that it indicates the density of data and generates the assumption of max pressure, medium, low, and small pressure relatively.
[x] Another bivariate distribution to indicate the outcome of X as Frame and Y as Max_tib_lat or Max_tib_Med to analyze the base of the outcomes on the frame.
[x] The outcomes of the above two graphs justify the random forests' outcome having higher stress at 100-150 grams. Therefore knowing the exact geometry position at frame 100-150, we can find the highest stress 3D plot and the issue of knees given that any number above 40's stress at 100-150 frames. will be the potential candidates.

cs481-ekh / f22-ai-cbl