Food and Exercise Recommender based on Caloric Intake and Food Preference

Being able to gain and lose weight is one of the biggest challenges people face when trying to improve their physical health, well-being, and/or appearance. It has proved extremely difficult for individuals to stay regimented and stick to not only their exercise routines, but to their dietary routines in order to achieve thier fitness goals.

According to an article by sundried.com, 95% of all New Year's goals are fitness related and of this 95%, only 10% thinks their goal will last after just 3 months. The article states that prime reasons for the lack of commitment to one's (fitness) goals stems from not having enough time, setting one's expectations too high, or not having a plan as to how one is going to go about their goal.

For fitness goals related to weight, health, and appearance, calorie count plays an enormous role in the progress seen, with a calorie surplus being used to gain weight and a calorie deficit being used to lose weight. By using data on the calorie count of various foods and the calorie burn of various exercises based on a person's weight, a user can keep track of their calories taken in, receive suggestions of food based on preference to hit their calorie counts, and can recieve the most effective form of exercise to burn calories and hit their daily calorie target, allowing them to reach their fitness goals much more easily.

import pandas as pd
df_food = pd.read_csv('calories.csv', index_col='FoodCategory')
df_food

	FoodItem	per100grams	Cals_per100grams	KJ_per100grams
FoodCategory
CannedFruit	Applesauce	100g	62 cal	260 kJ
CannedFruit	Canned Apricots	100g	48 cal	202 kJ
CannedFruit	Canned Blackberries	100g	92 cal	386 kJ
CannedFruit	Canned Blueberries	100g	88 cal	370 kJ
CannedFruit	Canned Cherries	100g	54 cal	227 kJ
...	...	...	...	...
Spreads	Sunflower Butter	100g	617 cal	2591 kJ
Spreads	Tapenade	100g	233 cal	979 kJ
Spreads	Unsalted Butter	100g	717 cal	3011 kJ
Spreads	Vegemite	100g	180 cal	756 kJ
Spreads	Wild Honey	100g	286 cal	1201 kJ

2225 rows × 4 columns

Content of df_food

FoodCategory: Type of food (CannedFruit, Vegetables, Fast Food, Cheese, Etc.)

FoodItem: Specific food

per100grams: Weight/Volume of food (100g or 100 mL)

Cals_per100grams: Calories in the food per weight/volume

KJ_per100grams: KiloJoules of energy in the food per weight/volume

df_exercise = pd.read_csv('exercise_dataset.csv', index_col = 'Activity, Exercise or Sport (1 hour)')
df_exercise

	130 lb	155 lb	180 lb	205 lb	Calories per kg
Activity, Exercise or Sport (1 hour)
Cycling, mountain bike, bmx	502	598	695	791	1.750730
Cycling, <10 mph, leisure bicycling	236	281	327	372	0.823236
Cycling, >20 mph, racing	944	1126	1308	1489	3.294974
Cycling, 10-11.9 mph, light	354	422	490	558	1.234853
Cycling, 12-13.9 mph, moderate	472	563	654	745	1.647825
...	...	...	...	...	...
General cleaning	207	246	286	326	0.721008
Cleaning, dusting	148	176	204	233	0.515199
Taking out trash	177	211	245	279	0.617427
Walking, pushing a wheelchair	236	281	327	372	0.823236
Teach physical education,exercise class	236	281	327	372	0.823236

248 rows × 5 columns

Content of df_exercise

Activity, Exercise or Sport (1 hour): Type of exercise

130 lb: Calories burned by 130 lb person doing exercise

155 lb: Calories burned by 155 lb person doing exercise

180 lb: Calories burned by 180 lb person doing exercise

205 lb: Calories burned by 205 lb person doing exercise

Calories per kg: calories burned per kg of weight of the individual

Data Usage in Solving the Problem

In order to help individuals stay on top of their daily calorie target, a user can input the foods they've eaten to count their calories or request to have foods given to them to meet their calorie count. Based on input foods, certain options will be weighted higher than others when finding foods to meet calorie count. When calorie count is exceeded, the most effective exercise to reach calorie goal will be provided based on user weight.