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I have this data frame with the years and values, can you make a model to predict the value of meats,fish,fruits and vegetables and

 
 I have this data frame 


image text in transcribed 
 with the years and values, can you make a model to predict the value of meats,fish,fruits and vegetables and grains? based on the current values of the table and historical data?
image text in transcribed 

food_imports_value - DataFrame Index US - Imports 2022 2021 2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 5 Live meat animals 2509.6 2298.9 2158.1 2253.4 2029.4 2016.3 2103.3 2774.9 3009.9 2193 6 Meats 7 Fish and shellfish 2/ 8 Dairy 9 Vegetables 10 Fruits 2196.3 1893.3 2014 14055.4 13194.1 10384.6 9668.9 9251.2 8875.2 8587.2 9991.7 8940.3 6529.9 6245.2 5755.3 5087.9 4612.1 30525.5 28493 21832.2 22469.1 22900.9 21924.9 19638.4 18877.9 20363.3 18059.6 16792.6 16509.3 14545.9 12954 2721.4 2459.5 2140.8 2195.7 2063.1 1876.9 1907.2 1911.5 1871.6 1712.1 1636.7 1524.5 1363.1 1366.2 15754.3 16838.2 15590.3 13974.8 13419.1 12670.5 12409.2 11227.1 10880.9 10687.6 9899.3 9645.4 8693.3 7512.8 27083.8 23668.8 20492.1 20313.8 19539.3 18384.6 17157.8 15954.8 14807.7 13602.3 12537.9 12158.6 10814 9799.9 5059.8 5367.4 13950.5 13466.4 1622.5 1540.8 7798.4 7254.6 1661.5 2277.1 2596.4 10064.7 9384.1 11 Nuts 2944.8 3157.4 2786.1 3191 3502.1 3312.2 2877.9 2776.1 2370 2019.4 2077.4 1881.6 1468.8 1285 1358.3 1190.8 12 13 Grains Coffee, tea, and spices 12980.6 9818.7 8346.2 8427.3 8454.7 9066.7 8311.3 8536.2 8298.8 7633.2 8725.7 19470 15255.9 13924.5 13035.5 12562.1 11257.6 10558.4 10360.1 10363.7 10584.6 9387.8 9715.8 6265 5160.3 5580.8 4791.2 8303.7 7348.1 7035.6 7902.3 6104.9 14 Vegetable oils 12850.8 9600.8 6682.1 6303.5 6989.2 7216 6523.3 6304.1 7336.6 6664.5 6235.9 6793.6 4508.8 4039.4 5694.6 3516.7 15 Sugar and candy 7074.8 5877.4 5318.3 4810.1 4803.9 4807.5 4802.9 4829.4 4705.4 4462.9 4931.8 5305 4185.2 3205.6 3108.1 2707.6 16 Cocoa and chocolate 6055.5 5634.1 5052.4 4977.1 4712.6 5012.6 5080.9 4859.9 4728.6 4159.5 4096.2 4681 4295.6 3476.4 3299.2 2661.6 17 Other edible products 29732.8 18825.4 16554.4 15270.5 14929.3 10871.7 11921.1 12105.3 12134.5 12178.2 13153.8 10141.6 8574.1 7613.6 8176.2 7415.8 18 Beverages 3/ 16076.5 19022.6 16368.9 16096.7 15760 15218.6 13558.9 12841.7 12013.7 11290.9 10894.4 10174.1 9293.2 8749.9 9859.6 9939.5

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