This project is a case study focused on predicting the probability of getting into an Ivy League college based on various factors. The analysis involves data exploration, preprocessing, and model building using Python libraries such as pandas, numpy, matplotlib, seaborn, and scikit-learn.
The main objective of this case study is to determine the probability of a student getting into an Ivy League college. The steps include:
- Analyzing the shape of the data and the data types of all attributes.
- Converting categorical attributes to the 'category' data type if required.
- Detecting missing values.
- Generating a statistical summary of the data.
- Building a predictive model.
App deployed on https://jamboree-prit.streamlit.app/
To run this project, you'll need to have Python installed along with several Python libraries. You can install the required libraries using pip:
pip install numpy pandas matplotlib seaborn scikit-learn- Clone the repository:
git clone https://github.com/yourusername/jamboree-case-study.git
- Navigate to the project directory:
cd jamboree-case-study - Run the Jupyter Notebook:
jupyter notebook Jamboree-Case\ Study-Copy1.ipynb - Follow the steps in the notebook to explore the data, preprocess it, and build a model.
Jamboree-Case Study-Copy1.ipynb: The main Jupyter Notebook containing the entire analysis and model building process.README.md: Project overview and setup instructions.
The dataset used in this case study is assumed to be available within the notebook. Ensure the data is loaded properly when running the notebook.
If you want to contribute to this project, please fork the repository and submit a pull request.
This project is licensed under the MIT License - see the LICENSE file for details.
- The project utilizes libraries such as
numpy,pandas,matplotlib,seaborn, andscikit-learnfor data analysis and model building. - Special thanks to Jamboree Education for providing the context for this case study.