This project aims to develop a powerful predictive model that accurately classifies students into two categories: those at risk of dropping out and those likely to succeed academically. Leveraging advanced data science techniques, we will identify essential factors that influence these outcomes, providing actionable insights to educational institutions for enhancing student support and overall academic success.
The primary objective of this project is to build a robust predictive model capable of accurately classifying students based on their risk of dropping out or academic success. Through comprehensive exploratory data analysis and feature engineering, we will identify and analyze key factors contributing to student dropout and academic performance.
To achieve our goals, we will adopt a machine learning approach. Initially, we will use the comprehensive dataset of student records, including demographic information, academic performance, socio-economic factors, and other pertinent features. After data cleaning and preprocessing to address errors, we will evaluate various machine learning algorithms to identify the most effective one for our dataset. Subsequently, we will employ the selected algorithm to build a predictive model that can classify new students into the desired categories.
To get started with the project, follow these simple steps:
- Clone the repository from GitHub:
git clone https://github.com/omar-kabeer/forecasting-academic-trajectories.git - Install the necessary Python packages using the
requirements.txtfile:pip install -r requirements.txt - Run the
app.pyfile to launch the Streamlit app.streamlit run app.py
Our user-friendly Streamlit app allows seamless interaction with the predictive model. Users can input student data, and the model will generate predictions regarding the student's risk of dropping out or likelihood of academic success.
This project is driven by a team of dedicated experts:
- Umar Kabir - Project Lead and Data Scientist
For further details about this project, please feel free to contact Umar Kabir at uksaid12@gmail.com