output screens, project output, python projects, machinelearning projects, data science projects, Artificial Intelegance projects, support vector machine, ramdom forest tree, Dession tree, linear Regression, logistic regression, Feature Selection, Naive Bayes, Machine learning Algorithms, Dimensionality Reduction Algorithms, Gradient Boosting algorithm, Logistic Regression, TensorFlow,Scikit-Learn,Numpy,Keras,PyTorch ,LightGBM, Eli5 ,SciPy,Theano,Pandas,imageProcessing Deep Learning,

1. Venkat Java Projects
Mobile:+91 9966499110
Visit:www.venkatjavaprojects.com Email:venkatjavaprojects@gmail.com
RecommenderSystem with Artificial Intelligence for Fitness Assistance
System*
In this project author is describing concept to recommend new users with
Fitness Assistance System (FAS) using Artificial Neural Network Algorithm. The
RS (Recommender System) is applied to make these suggestions for the
beginners and existing users. The goal of the paper aims to develop an RS that
has an ability to learn, analyse, predict, and make these suggestions as well as
communicate to human through AI. Artificial Neural Network and Logistic
Regression have been employed to predict the suitable workout for each
beginner. The purposeof this paper concept is to design the RS that will suggest
personalized workout to the users and predict the plan for doing exercise in
future. In the proposed RS, we usemachine learning algorithms on activity data
to build a predictive module in the basic training layer (BTL) that classify the
user’s activity in their workout. In addition, we also build the trainer agent (TA)
with Soar architecture and machine learning algorithm to reflect the prediction
of BTL for suggesting the several workouts to help users select the suitable
workout fitting well with their exercise plan.
Using ANN algorithm we will build train model on past users data using various
features suchas Age, Gender,Height, Weight, ExerciseTypeand One-Repetition
Maximum (1-RM). After building train model, a new user data can be given as
input (Age, Gender, Height and Weight) to ANN model and ANN model will
predict best 1-RM, require weight lifting for exercise and number of breaks as
output. This output can be consider as Recommended Exercise for new user.
Example values form dataset to train ANN model
Gender, Age, Height, Weight, Exercise_Type, RM
0, 41, 171, 60, 2, 60
0, 32, 174, 103, 2, 103
0, 22, 159, 58, 1, 54
1, 46, 192, 60, 1, 54

2. Venkat Java Projects
Mobile:+91 9966499110
Visit:www.venkatjavaprojects.com Email:venkatjavaprojects@gmail.com
In above dataset we can see all past user’s data and last column value ‘RM’
consider as the target class value which ANN predicts for new users.
Below are some test values of new users
Gender, Age, Height, Weight, Exercise_Type
0, 41, 171, 60, 2
0, 32, 174, 103, 2
0, 22, 159, 58, 1
0, 46, 192, 60, 1
1, 40, 171, 79, 2
Inabovetestdata RM valueis notthere and ANNwillpredict RMvaluefor above
test data. Once we got RM value then we can compute recommended exercise
for new users. See below example
Inabovetable if predicted RM< 67then wecan get Diet as15 to 20as Repetition
and exercise type can be 1, 2 or c and break can up to 4 minutes. Similarly if RM
> 67 then we will get other output for Muscle Up.
ANN Working Procedure
An artificial neuron network (ANN) is a computational model based on the
structure and functions of biological neural networks. Information that flows

3. Venkat Java Projects
Mobile:+91 9966499110
Visit:www.venkatjavaprojects.com Email:venkatjavaprojects@gmail.com
through the network affects the structureof the ANN becausea neuralnetwork
changes - or learns, in a sense - based on that input and output.
ANNs are considered nonlinear statistical data modelling tools where the
complex relationshipsbetween inputs and outputs aremodelled or patterns are
found.
ANN is also known as a neural network.
An ANN has several advantages but one of the most recognized of these is the
fact that it can actually learn from observing data sets. In this way, ANN is used
as a random function approximation tool. These typesof tools help estimate the
most cost-effective and ideal methods for arriving at solutions while defining
computing functionsor distributions.ANNtakes data samples rather than entire
data sets to arrive at solutions, which saves both time and money. ANNs are
considered fairly simple mathematical models to enhance existing data analysis
technologies.
ANNs have three layers that are interconnected. The firstlayer consists of input
neurons. Those neurons send data on to the second layer, which in turn sends
the output neurons to the third layer.
Screen shots
Double click on ‘run.bat’ file to get below screen

4. Venkat Java Projects
Mobile:+91 9966499110
Visit:www.venkatjavaprojects.com Email:venkatjavaprojects@gmail.com
In above screen click on ‘Upload Fitness Dataset’ button to upload dataset

5. Venkat Java Projects
Mobile:+91 9966499110
Visit:www.venkatjavaprojects.com Email:venkatjavaprojects@gmail.com
In above screen uploading fitness dataset file called ‘dataset.csv’, after
uploading dataset click on ‘Read Dataset & Generate Train Test Model’ button
to read dataset and to generate train and test model for ANN.
After reading dataset we got above screen, in above screen we can see total
number of records available in dataset and number of records used for training
and testing. Now click on ‘Run Ann Algorithm’ button to apply ANNalgorithm on
train and test data. After applying ANN we will get ANN model to
predict/recommend new users exercise plan.

6. Venkat Java Projects
Mobile:+91 9966499110
Visit:www.venkatjavaprojects.com Email:venkatjavaprojects@gmail.com
In abovescreen after building ANN model we got its accuracy as 62%. Now click
on ‘Predict Workout for New Users’ button to upload new users test data and
to recommend exercise for those new users

7. Venkat Java Projects
Mobile:+91 9966499110
Visit:www.venkatjavaprojects.com Email:venkatjavaprojects@gmail.com
In above screen uploading new users test.csv file to recommend exercise plan
for news users. After upload will get below recommendation
In above screen for each user we got recommendation as require exercise
weight, repetition and break time. Infirstrecordwecan see requirerecommend
weight is 60 and repetition is 15 to 20 and break time is 4 minutes. Similarly for
all new users we got recommended exercise plan.
Note: If u want u can add new users data inside ‘dataset/test.csv’ file and
application will predict/recommend for those new user’s records also.