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People are not paying attention to the quality of food they eat in our fast-paced and hectic world. They frequently ignore their eating routines and behaviours. Fast-food consumption is frighteningly increasing, which has resulted in the consumption of harmful foods. This causes a variety of health problems, including obesity, diabetes, and an increase in blood pressure, and so forth. As a result, it has become critical for people to have a well-balanced nutritionally sound diet. There are several applications that are thriving to assist folks in gaining control of their food and therefore can help individuals lose weight or maintain their fitness and health. The study article proposes healthy eating habits and patterns so that anybody may know the number of calories expended, macronutrient intake, and so on using data mining technologies. This technology is designed to uncover hidden patterns and client eating habits from various data sources. This approach will aid in tracking and improving an individual's health as well as the types of food that they should avoid in order to reduce their chance of disease. A balanced diet is one in which the intake of each basic nutrient meets its sufficient demand and real caloric intake equals calories burnt. Additionally, making a variety of dietary choices is vital for lowering the chance of acquiring chronic illnesses. This diet recommendation system tailors its recommendations to each individual depending on their eating patterns and body data. This study aids in the prediction of a healthy diet for any individual, as well as the construction of a diet plan based on the needs of the patient. Keywords : BMR, Healthy Diet, Recommender System, Harris Benedict equation, Nutrition, Calories, Data mining

Oil and natural gas production has been an important economic booster in the recent past. However, unconventional methods have risen environmental health concerns. This study presents preliminary results of heavy metal concentrations in water (surface and groundwater) samples around oil and natural gas drilling sites in East-West Godavari districts of A.P, India. A total of 36 samples, 24 surface water (SW) and 12 groundwater (GW) were collected to evaluate the distribution of pH, EC, TDS, As, Cd, Cr, Cu, Mo, Ni, Pb, Zn, and radiogenic elements (U, Th). Results acquired were treated with principal component analysis (PCA)/factor analysis (FA), hierarchical cluster analysis (HCA), and regression coefficient analysis to identify a collective contamination source. Mean concentrations obtained for surface and groundwater were 7.60 and 7.34 for pH; 4048 and 2964 mg/L for TDS; 8.50 and 5.91 µs/cm for EC; 11.5 and 10.7 μg/L for As; 14.6 and 10.8 μg/L for Cr; 0.60 and 0.70 μg/L for Cd; 18.6 and 29.1 μg/L for Cu; 3.00 and 4.20 μg/L for Mo; 19.9 and 24.8 for Ni; 15.2 and 13.4 μg/L for Pb; 4.60 and 3.10 μg/L for Th; 1.00 and 8.30 μg/L for U; 187 and 348 μg/L for Zn respectively. FA recognized four factors accountable for data structure elucidating 86.23 % of the total variance in SW, four factors in GW explaining 91.6%, and permitted to assemble particular parameters based on collective features. As, Cd, Cu, Mo, Pb, and U were linked and well-ordered by diverse origin with related influence from anthropogenic and geogenic sources. This study describes the importance and efficacy of multivariate statistical methods for evaluation and understanding the data to get better evidence about the water quality and project some mitigation methods to avert the contamination affected by harmful heavy metals in the future.