GrandGravity

  Introduction Last year, as a certified AI specialist from the Samsung AI Innovation Campus, I embarked on an exciting venture with Samsung to develop an AI-driven application specifically tailored for students at the Central University of Technology (CUT) in Bloemfontein. Our team, utilizing Python, set out to craft innovative AI models aimed at revolutionizing the educational experience. This post explores the numerous challenges we encountered, the innovative solutions we implemented, and the profound impact our project had on the student community. Background or Challenge The primary goal of this project was to develop an application that leveraged advanced AI to assist CUT students with their academic and campus activities. Addressing the diverse needs of the student body posed a significant challenge: creating a tool that was both adaptable and intuitive for all users. Moreover, the project was constrained to a tight timeline of five months, during which we needed to design,...

  pip install streamlit We Run this command to install streamlit dependencies in our program import streamlit as st # Define function to predict academic assistance need based on learner details def predict_academic_assistance ( age , gender ):     # Simple rule-based prediction:     # If the learner is under 18 years old or if the learner is female, predict that academic assistance is needed     if age < 18 or gender == "Female" :         return 1   # Academic assistance likely needed     else :         return 0   # Academic assistance not likely needed # Define Streamlit application def main ():     st.title( "Academic Assistance Predictor" )     # Collect learner details from user input     age = st.number_input( "Enter Age" , min_value= 0 , max_value= 120 , step= 1 )     gender = st.selectbox( "Select Gender" , [ "Male" , "Female" ]) ...

   ! pip install Faker import pandas as pd import numpy as np from faker import Faker import random # Initialize Faker to generate fake data fake = Faker() # Define the number of student records to generate num_students = 1000 # Generate synthetic data for student records data = {     'Student_ID' : [fake.unique.random_number(digits= 8 ) for _ in range (num_students)],     'Age' : [random.randint( 18 , 25 ) for _ in range (num_students)],     'Gender' : [random.choice([ 'Male' , 'Female' ]) for _ in range (num_students)],     'Ethnicity' : [fake.random_element(elements=( 'White' , 'Black' , 'Asian' , 'Hispanic' )) for _ in range (num_students)],     'Parental_Education' : [fake.random_element(elements=( 'High School' , 'Bachelor' , 'Master' , 'PhD' )) for _ in range (num_students)],     'Household_Income' : [random.randint( 20000 ...

 Project Outline: Academic Performance Analysis and Assistance Predictor Introduction: In today's educational landscape, understanding student performance and identifying those who may need additional support are crucial tasks for educators. This project delves into the realm of academic performance analysis and aims to develop a predictive model to assist educators in identifying students who may require academic assistance. By leveraging data-driven insights and machine learning techniques, we seek to provide a tool that can aid educators in making informed decisions to support student success. Data: The foundation of this project lies in a comprehensive dataset encompassing various attributes related to student academic performance. This dataset, meticulously curated and preprocessed, provides valuable insights into factors influencing student success. From demographic information to past academic achievements, the dataset offers a holistic view of student profiles, enabling mea...