Hi - I'm working on a school project and I need to take the attached data set and build a model to understand which features are important to predict two response variables (Test A and Test B). These columns are two separate experiments of mixing protein powder and measuring the best mixed (0% being the liquid and the solid didn't mix, 100% perfectly mixed). This model needs to tell me which features are the most important to achieve a high mixability. The binary columns just indicate if a brand has a presence of that ingredient. I'm new to this, and I'm looking for pointers on how to get started and what my strategy should be. I'm comfortable in R or Python and have done a fair bit of basic data analysis (especially in R), mostly linear and logistic regression.

Test A	Test B	Total Fat (g)	Saturated Fat (g)	Sodium (mg)	Total Carbs (g)	Dietary Fiber (g)	Total Sugars (g)	Protein (g)	Whey Isolate	Whey Concentrate	Whey Hydrolysate	Whey Peptides	Sucralose	Ace-K	Natural Sweetener	Alkalized Cocoa Powder	Sunflower Lecithin	Soy Lecithin	Maltodextrin	Cellulose Gum	Xanthan Gum	Gum Arabic	Guar Gum	Rice Powder	Soluble Corn Fiber	Modified Food Starch	Carrageenan	Salt	Anti-Caking Agent	Digestion Enzymes	Vitamins or Minerals	[50 Blows] Density (g/mL)	Vibration Density (g/mL)	Avg Particle Size
85%	75%	0.6	0.0	60.6	1.8	0.3	0.6	15.2	1	1	0	0	0	0	1	1	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0.42	0.39	170
85%	35%	0.6	0.0	41.9	1.3	0.3	0.6	16.1	1	1	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0.46	0.40	184
30%	90%	1.8	0.9	84.4	3.6	0.4	1.1	13.3	1	1	0	0	1	1	0	1	1	0	1	1	0	0	0	0	0	0	0	1	0	0	1	0.46	0.41	158
40%	45%	1.4	0.7	72.7	3.2	0.0	0.9	13.6	1	1	0	0	1	1	0	0	1	0	1	1	0	0	0	0	0	0	0	1	0	0	1	0.44	0.40	190
90%	95%	1.2	0.6	94.1	1.8	0.0	1.2	14.7	1	1	0	0	1	1	0	1	0	1	0	0	0	0	0	0	0	0	0	1	0	1	1	0.52	0.48	145
95%	65%	1.2	0.6	84.8	1.8	0.0	1.2	15.2	1	1	0	0	1	1	0	0	0	1	1	0	0	0	0	0	0	0	0	1	0	1	1	0.46	0.38	149
95%	100%	1.1	0.6	5.7	1.1	0.0	1.1	13.7	0	1	0	0	0	0	1	1	1	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0.40	0.37	156
90%	95%	1.2	0.5	90.5	4.3	0.0	1.0	11.9	1	1	0	0	1	1	0	1	1	0	1	1	1	0	0	0	0	0	0	1	0	0	1	0.49	0.44	160
40%	50%	1.0	0.5	48.0	4.0	0.0	0.5	12.6	1	1	0	0	1	1	0	0	1	0	1	1	1	0	0	0	0	0	0	0	0	0	1	0.47	0.42	190
95%	100%	1.0	0.5	82.9	1.5	0.5	0.5	14.6	1	1	0	0	1	1	0	1	1	0	0	0	1	0	0	0	1	0	0	1	0	0	0	0.50	0.45	133
70%	30%	1.0	0.5	87.2	1.0	0.0	0.5	15.4	1	1	0	0	1	1	0	0	1	0	0	0	1	0	0	0	1	0	0	1	0	0	0	0.46	0.41	149
60%	65%	1.0	0.5	85.0	2.0	0.5	0.5	15.0	1	1	0	0	1	1	0	0	1	0	0	0	1	0	0	0	1	0	0	1	0	0	0	0.48	0.43	145
85%	100%	1.6	0.8	60.0	2.4	0.4	0.8	14.4	0	1	0	0	1	1	0	1	1	0	0	1	0	1	0	0	0	0	0	1	0	0	0	0.42	0.39	166
65%	70%	0.0	0.0	32.0	1.6	0.0	1.6	16.0	1	0	0	0	1	1	0	1	1	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0.43	0.37	162
70%	80%	1.2	0.8	32.0	3.2	0.0	0.8	14.4	0	1	0	0	1	1	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0.42	0.39	192
95%	85%	1.2	0.8	40.0	2.4	0.0	0.8	14.4	0	1	0	0	1	1	0	1	1	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0.47	0.45	168
35%	75%	1.2	0.9	90.9	1.2	0.0	1.2	14.5	1	1	0	1	1	1	0	1	0	1	0	1	1	0	0	0	0	0	1	1	1	0	0	0.43	0.38	152
95%	35%	1.2	0.9	75.8	1.2	0.0	1.2	14.5	1	1	0	1	1	1	0	0	0	1	0	1	1	0	0	0	0	0	1	1	1	0	0	0.42	0.35	177
80%	80%	1.3	0.6	81.3	1.9	0.3	1.3	15.0	1	1	1	0	1	1	0	1	0	1	1	0	0	0	0	0	0	1	0	1	1	0	1	0.44	0.39	171
95%	95%	1.0	0.3	32.9	2.0	0.3	0.7	15.8	1	1	0	1	0	1	0	1	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0.54	0.43	148
65%	45%	1.0	0.6	83.9	1.3	0.0	1.3	15.5	1	1	1	0	1	1	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	0	0.39	0.37	188
70%	75%	0.6	0.3	74.2	1.9	0.0	0.6	15.5	1	1	0	1	1	1	0	0	0	1	0	1	1	0	0	0	0	0	0	1	0	1	0	0.41	0.37	186
100%	90%	1.3	0.6	81.3	1.9	0.0	0.6	15.0	1	1	1	0	1	1	0	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0.46	0.43	152