I need java CODE that takes this data

Rk$,$Player,Pos,Age,Tm$,$G$,$GS$,$MPG$,$FG$,$FGA,FG$\%,3$PM$,3$PA$,3$P$\%,2$PM$,2$PA$,2$P$\%,$eFG$\%,$FT$,$FTA,FT$\%,$ORB,DRB$,$TRB$,$AST,STL$,$BLK$,$TOV,PF$,$PTS

$1,$Precious Achiuwa,C$,23,$TOR,$55,12,20\mathrm{.}7,3\mathrm{.}6,7\mathrm{.}3,0\mathrm{.}485,0\mathrm{.}5,2,0\mathrm{.}269,3,5\mathrm{.}4,0\mathrm{.}564,0\mathrm{.}521,1\mathrm{.}6,2\mathrm{.}3,0\mathrm{.}702,1\mathrm{.}8,4\mathrm{.}1,6,0\mathrm{.}9,0\mathrm{.}6,0\mathrm{.}5,1\mathrm{.}1,1\mathrm{.}9,9\mathrm{.}2$

$2,$Steven Adams,C$,29,$MEM,$42,42,27,3\mathrm{.}7,6\mathrm{.}3,0\mathrm{.}597,0,0,0,3\mathrm{.}7,6\mathrm{.}2,0\mathrm{.}599,0\mathrm{.}597,1\mathrm{.}1,3\mathrm{.}1,0\mathrm{.}364,5\mathrm{.}1,6\mathrm{.}5,11\mathrm{.}5,2\mathrm{.}3,0\mathrm{.}9,1\mathrm{.}1,1\mathrm{.}9,2\mathrm{.}3,8\mathrm{.}6$

and does the following functionalities

Functionality $1$: Players Stats and Minutes Played

Analyzing NBA players' performance goes beyond basic stats. This exploration utilizes player

data to predict their rebounds, assists, points, and steals per game based on average minutes

played. We will build a Random Forest Regression model using Java libraries. This model is

better suited than linear regression for capturing the non$-$linear relationship between minutes

played and these stats. While factors like talent and opponent strength influence performance,

this approach provides a data$-$driven analysis to estimate a player's potential based on average

minutes played.

Functionality $2$: Win Share Data for Players

We can use a multi$-$step approach in Java to estimate a player's Win Shares $($WS$),$ a metric of

their contribution to winning. First, we'll create new features by combining existing stats $($FG$\%,$

rebounds, etc.$)$ to capture aspects like scoring efficiency or rebounding rate that might influence

wins. Then, we'll build a Random Forest Regression model, a prediction tool. This model will be

trained on existing player data, but instead of using actual Win Shares, it will learn the

relationships between stats and Win Shares using a formula like John Hollinger's $($The creator of

the original win share formula$).$ By analyzing these patterns, the model can predict Win Shares

for new players based solely on their provided stats. Remember, this is an estimate of a player's

true impact on winning depends on various factors beyond just individual stats.

third functionality, we would like to see a performance evaluation of the players. A user should be able to select a player and view whether they or under$-$ or over$-$performing relative to their contract, and by how much $($percentage$).$ This will likely involve a regression model that takes individual statistics $($counting and$/$or advanced$)$ and produces a monetary value for it$.$ You would have to obtain annual contract data for players to achieve this.