Project Title: Funds Distribution Project - Optimizing Education Impact
Demo:
A live demo of the app is available at https://schoolfundsdistribution.streamlit.app/.
Description :
This project empowers an ed-tech company to achieve maximum impact in improving the current education landscape by:
- Identifying key constraints: Analyzing factors hindering educational progress.
- Data-driven fund allocation: Strategically distributing resources to address these constraints.
By focusing on these elements, we ensure that funds reach to 50000 schools with the greatest need, fostering equitable access to quality education.
Key Technologies:
1.Python (programming language) 2.Streamlit (web framework) 3.Pandas (data manipulation library) 4.Matplotlib , Seaborn for visualizations 5.Numpy and Scipy for numeric and scientific calculations
Dataset used :
Used Input_Schools_Data.csv in funds allocation process Allocated funds to schools in the Output_Schools_Data.csv
Project Structure:
1.data: Contains raw and cleaned school data
2.src: Contains Python scripts for: data_cleaning.py: Data cleaning functions to prepare the dataset. rsc_allocation.py: Functions for fund allocation calculations and details. eda.py: Functions for exploratory data analysis visualizations. app.py: The main Streamlit app script.
3.notebook : Jupyter notebook of whole process
4.requirements.txt: Lists dependencies required to run the project.
Installation:
Clone this repository: git clone https://github.com/mhuzaifa5/School_Funds_Distribution.git
Install dependencies: pip install -r requirements.txt
Running the App:
Navigate to the project directory: cd School_funds_distribution Run the Streamlit app: streamlit run app.py
Future Improvements: The app is a little slow due to dense calculation in each iteration . So the main task is to make it as fast as possible.