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  Published Paper Details:

  Paper Title

Review On Diet Recommendation System Using Machine Learning

  Authors

  Trupti Bhagat,  Prof. Ranu Tuteja

  Keywords

Machine learning, Diet Recommendation, Food recommendation

  Abstract


In today's modern world people all around the globe are becoming more interested in their health and lifestyle. But just avoiding junk food and doing an exercise is not enough, we require a balanced diet. A balanced diet based on our height, weight and age can lead a healthy life. Combined with physical activity, your diet can help you to reach and maintain a healthy weight, reduce your risk of chronic diseases (like heart disease and cancer), and promote your overall health. A balanced diet is one that gives your body the nutrients it needs to function correctly. Calories in the food is the measure of amount of energy store in that food. Our body use calories for basically everything like breathing, walking, running etc. On average a person needs 2000 calories per day but specifically intake of calories depends upon persons physical aspects like weight, height, age and gender. So, your food choices each day affect your health -- how you feel today, tomorrow, and in the future. Thus, a proposed system gives recommend you a diet plan based on your physical aspects and your end goal.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2303881

  Paper ID - 233438

  Page Number(s) - h427-h431

  Pubished in - Volume 11 | Issue 3 | March 2023

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Trupti Bhagat,  Prof. Ranu Tuteja,   "Review On Diet Recommendation System Using Machine Learning", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.11, Issue 3, pp.h427-h431, March 2023, Available at :http://www.ijcrt.org/papers/IJCRT2303881.pdf

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ISSN: 2320-2882
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Journal Starting Year (ESTD) : 2013
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ISSN and 7.97 Impact Factor Details


ISSN
ISSN
ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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