The document discusses Mohammed Taha Ahmed Al daghan's four week internship at ASPEXX Health Solutions Pvt Ltd. It covered topics in artificial intelligence, machine learning, data science, and mobile app building. Projects included a personalized product recommendation tool using collaborative filtering, age prediction using OpenCV, and building mobile apps with Flutter and Ionic frameworks. The internship provided hands-on experience with real-world datasets and guidance from mentors.

1. DATA SCIENCE & MACHINE LAERNING
Presented by
Mohammed Taha Ahmed Al daghan
information science Department
AMC Engineering College

2. A PROOF OF CONCEPT
on
AI, ML , Data Science and and Mobile
App building
Four Weeks Internship
ASPEXX Health Solutions Pvt Ltd.

3. Introduction
 Aspexx Health 'deals with various' aspects' of healthcare .
 Aspexx Health 'aims to achieve- affordable, equitable and quality, healthcare digital solutions
 based on 'Integrated Healthcare system’ .
 The objective is to eliminate the information asymmetry by dissemination of healthcare education
 related to Alternative treatments for serious ailments .
 Further to this, the platform facilitates with option of - Consultation with
 ( doctors, Councilors, Nurse, dietitian) and Healthcare products .

4. Internship outcomes
 Student learns the concept of Data Science and Analytics.
 Good environment and flexible working hours
 Projects are based on real world dataset, and proper guidance at every step.
 Mentors and team leaders are always supportive and create friendly environment with interns.
 Enhance a interview skills and helps to gain a real world industry experience.
 Provide the internship completion certificate and Letter of recommendation (LOC).

5. Topics Covered
Artificial Intelligence
Machine Learning
Data Science
Mobile App building

6. Project Undertaken
Artificial Intelligence:
• Personalized product recommendation
Data Science:
• Age prediction using OpenCV
Mobile App building:
• Movie rating app using Ionic freamwork
• Book doctor appointment app using Flutter freamwork

7.  Product recommendations are part of an
ecommerce personalization strategy wherein
products are dynamically populated to a user on a
webpage, app, or email based on data such as
customer attributes, browsing behavior, or
situational context—providing a personalized
shopping experience.
Personalized Product Recommendation
Tool

8. Dataset
 The data set contain 7,824,482 rows and 4 columns:
• User ID
• item ID
• Rating
• TimeStamp

9. Collection:
• Data collected here can be either explicit such as data fed by users (ratings
on products) .
Storing:
• The type of data you use to create recommendations can help you decide
the kind of storage you should use,like (user ID,item ID & ratings)
Analyzing:
• The recommender system analyzes and finds items with similar user engagement
data by filtering it using different analysis methods such as batch analysis
Filtering
• The last step is to filter the data to get the relevant information
required to provide recommendations to the user
Working

10. Tools and Working Environment
 It includes data manipulation and visualization libraries such as:-
• Pandas
• NumPy
• Matplotlib
• Seaborn
 Dependencies
• The dependencies that could be involved throughout the project is Python 3.5 and above and installation of all
libraries using conda prompt.
• Installation of Anaconda and Jupyter Notebook.

12. Age prediction using
OpenCV
• Age estimation can be defined as the automatic process of classifying
the facial image into the exact age or to a specific age range
• Processing of the image based on analysis undergoes many different
techniques and calculations.

13. Working
The code can be divided into four parts:
Detect Face:
• We used the DNN Face Detector for face detection
Predict Gender:
• load the gender network into memory and pass the detected face
through the network
Predict Age:
• load the age network and use the forward pass to get the output
Display Output:
• display the output of the network on the input images and show them
using the imshow function.

14. Tools and Working Environment
 It includes data manipulation and visualization libraries such as:-
• OpenCV
• Math
• Command-line parsing
 Dependencies
• The dependencies that could be involved throughout the project is Python 3.5 and
above and installation of all libraries using conda prompt.
• Installation of Anaconda and Jupyter Notebook.

16. Mobile Application
Flutter:
• It is an open-source UI software development kit created by Google
• It is used to develop cross platform applications for Android, iOS, Linux, macOS,
Windows,Google Fuchsia, and the web from a single codebase
Ionic:
• Ionic is a complete open-source SDK for hybrid mobile app development
• Ionic empowers web developers to build leading cross-platform
mobile apps and Progressive Web Apps (PWAs)

19. References
 https://www.kaggle.com/uciml/breast-cancer-wisconsin-data/tasks?taskId=299
 https://www.w3schools.com/python/python_intro.asp
 https://numpy.org/doc/stable/user/quickstart.html
 https://www.investopedia.com/terms/s/social-media.asp
 https://www.investopedia.com/terms/m/marketing.asp
 https://docs.python.org/3/reference/
 https://numpy.org/doc/stable/user/quickstart.html