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| # Smart Fitness & Nutrition Analytics Dataset | |
| ### By Idan Khen | |
| ## Project Summary | |
| This project explores a fitness and nutrition dataset to understand what affects the number of calories people burn during exercise. | |
| The goal was to clean the data, create a few visualizations, and answer simple questions about calorie burn, BMI, gender, heart rate, and protein intake. | |
| The results help show which factors matter and which do not. | |
| ## Dataset Information | |
| The dataset is a created fitness and nutrition dataset with 20,000 rows. | |
| It includes features like age, gender, weight, height, calories burned, workout frequency, protein intake, and average BPM. | |
| Before the analysis, the data was cleaned. | |
| I corrected unrealistic values and removed entries with missing or impossible values. | |
| This helped make the final results more reliable. | |
| The goal of the project was to answer simple questions about what affects calories burned, such as gender, BMI, protein intake, and heart rate. | |
| ## Data Cleaning Process | |
| Before starting the analysis, several corrections were made to improve the quality of the dataset: | |
| - Fixed unrealistic numeric values by converting implossible numbers(such as negative suger, sodium etc) into NaN | |
| - Handled broken columns by completely removing the column "Burn Calories (per 30 min)_bc" which contained unusable data | |
| - Cleaned text fields by removing placeholder values(such as "na","none" , etc). | |
| - Filled missing valies created during cleaning using each column's median. | |
| ## Outlier Handling | |
| Some values were unusually high or low compared to the rest. | |
| These extreme points could distort the graphs, so they were reviewed: | |
| - Outliers in calories burned, protein intake, and weight were checked. | |
| - Only values that could realistically happen in real life were kept. | |
| - Impossible values or data entry mistakes were removed. | |
| This helped make all visualizations more accurate and easier to understand. | |
| # QUESTIONS & ANSWERS | |
| ## 1.Do males and females burn different amounts of calories? | |
| Men and women burn almost the same amount of calories. | |
| The boxplot shows that both groups have a very similar median and a similar spread. | |
| This means gender does not play an important role in calories burned. | |
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| ## 2.Does BMI influence calories burned? | |
| There is no clear relationship between BMI and calories burned. | |
| People with low BMI and high BMI burn similar amounts of calories. | |
| The scatter plot is very spread out, which shows that BMI alone does not explain how much energy someone burns. | |
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| ## 3.Does protein intake influence calories burned? | |
| Protein intake does not influence how many calories a person burns. | |
| The scatter plot shows no pattern, so eating more protein does not mean burning more calories | |
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| ## 4.Does average BPM reflect calories burned? | |
| There is no strong connection between average heart rate and calories burned. | |
| People with higher BPM do not always burn more calories. | |
| The points are very spread out, showing a weak relationship. | |
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| # Overall Insights | |
| From all graphs, the main insight is that calories burned are not strongly affected by BMI, gender, protein intake, or average BPM. | |
| The dataset shows that calorie burn depends more on workout habits and effort, not on physical or nutrition details. | |
| # Decisions | |
| If someone wants to predict calories burned, they would need more detailed workout information like workout intensity, training style, and session duration. | |
| These factors would likely give better predictions than BMI or protein intake. | |