The data set we have taken is about calculating body fat percentage for an individual.

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1 The Process we are mining: The data set we have taken is about calculating body fat percentage for an individual. What is Body Fat percentage? The body fat percentage (BFP) of a human or other living being is the total mass of fat divided by total body mass. The body fat percentage is a measure of fitness level. Finding the Body fat percentage is a complicated process. Percentage of body fat for an individual can be estimated once body density has been determined and using that value in Siri's equation. Volume, and hence body density, can be accurately measured a variety of ways. The technique of underwater weighing computes body volume as the difference between body weight measured in air and weight measured during water submersion. In other words, body volume is equal to the loss of weight in water with the appropriate temperature correction for the water's density. So we are going to predict Body fat percentage using different attributes such as height, weight, age, and circumference value of various body parts which can be simpler process then calculating body density. Variables in our Analysis: The body density dataset includes the following 15 variables listed from left to right: Density determined from underwater weighing Percent body fat from Siri's (1956) equation Age (years) Weight (lbs.) Height (inch) Neck circumference (inch) Chest circumference (inch) Abdomen 2 circumference (inch) Hip circumference (inch) Thigh circumference (inch) Knee circumference (inch) Ankle circumference (inch) Biceps (extended) circumference (inch) Forearm circumference (inch) Wrist circumference (inch) Data Source: 1 P a g e

2 Figure 1 Source: Starting our Analysis: We check through the data and found out that the data has relevant information that can be used for data mining. Step 1: Think First and Mine Later: The problem statement here that we need to predict the body fat percentage using different attributes of an individual. Step 2: Identify Target Variable: The target variables here is Percent body fat from Siri's (1956) equation. The Target variable is a percentage value and it is quantitative and perfectly suits for analysis. Step 3: Format the data: The dataset we had has units of measure in kg for weights and cm for Height, Neck circumference, Chest circumference, Abdomen 2 circumference, Hip circumference, Thigh circumference, Knee circumference, Ankle circumference, Biceps (extended), circumference, Forearm circumference, Wrist circumference (source must possibly from Europe!!). So we have to format this data and change the units of all these values to standard units used in United States of America (As our clients are Americans). Degrees of freedom = 252 (Observation) 13 (Variables) = 239 Step 4: Identify Driver Variables: The Driver variables here are Age, Weight, Height, Neck circumference, Chest circumference, Abdomen 2 circumference, Hip circumference, Thigh circumference, Knee circumference, Ankle circumference, Biceps (extended), circumference, Forearm circumference, Wrist circumference. We didn t use Density determined from underwater weighing variable because it s a proxy variable which is used to determine our target variable Percent body fat from Siri's (1956) equation. 2 P a g e

3 Figure 2 Step 3: Format the data Step 5 & 6: Acquire and analyzing our data: There was no error, blank columns, bad data or outliers in the data file. Step 7: Transform the data: The cross sectional data we have taken gives us proper description of data. There was no proper requirement for large transformation of data. Step 8: Mine The data: As the data being ready, we started mining the data set using neural network. Neural network architecture, which is also referred to as artificial intelligence, that utilize predictive algorithms. This technology has many similar characteristics to that of regression in that the application generally examines historical data, and utilizes a functional form that best equates explanatory variables and the target variable in a manner that minimizes the error between what the model had produced and what actually occurred in the past and then applies this function to future data. Leaving the Proxy variable Underwater Density, we plot Body fat percentage in Y axis and all the other columns in X axis. Setting holdback at 0.33 for the model to apply values to predict target in data sets and also setting hidden nodes value as 3. We run the model, the output is shown below. 3 P a g e

4 Results: We have got a reasonable R 2 value for both data set used for modelling and data used for predicting. Now using profiler we can adjust the driver variables using the graph and get the corresponding body fat percentage. This profiler graph or formula result from modelling can be used to predict the body fat percentage of any individual. Seeing the graphs in profiler we can see that Abdomen 2 circumference, wrist circumference, weight and forearm circumference have significant impact on body fat percentage. Using the Significant variable we run the neural network again using driver variable as Abdomen 2 circumference, wrist circumference, weight and forearm circumference. We still able to obtain significant R 2 value for this model. 4 P a g e

5 For cross verification we run the same data set with Regression with all the driver variables except body density. Then later, we removed all insignificant driver variables one by one with t stat value in between -2 to 2 and with high p value. Then we found that Abdomen 2 circumference, wrist circumference, weight and forearm circumference have significant impact on body fat percentage without reducing R 2 square value. 5 P a g e