final year internship ppt

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