In this project, we are provided with a limited dataset containing information about students currently enrolled in a specific program at a reputed university. The data includes personal details and curriculum data. The goal is to leverage Data Science techniques to perform a detailed Exploratory Data Analysis (EDA) and build a prediction model. The primary objective of the model is to predict whether a college student will graduate or not.
Perform an in-depth Exploratory Data Analysis on the provided student dataset. This includes understanding the distribution of data, identifying patterns, and gaining insights into the relationships between different variables.
Utilize at least five machine learning algorithms to build an efficient prediction model. The model aims to predict the likelihood of a student graduating based on the available features. The algorithms employed will be carefully chosen to ensure robust performance and generalization.
Apply SHAP (SHapley Additive exPlanations) analysis on the dataset. SHAP values provide a way to interpret the output of machine learning models and understand the impact of each feature on the model's predictions. This analysis will enhance our understanding of the model's decision-making process.
-
Exploratory Data Analysis (EDA):
- Analyze the distribution of variables.
- Identify and handle missing data.
- Visualize relationships between features.
- Extract meaningful insights from the data.
-
Machine Learning Model Development:
- Implement at least five machine learning algorithms for prediction.
- Evaluate and compare the performance of each algorithm.
- Visualize model performance using confusion metrics and AUC-ROC curves.
-
SHAP Analysis:
- Apply SHAP analysis to interpret the model's predictions.
- Understand the contribution of each feature to individual predictions.
- Provide meaningful insights derived from the SHAP analysis.
Summarize the key findings from the EDA, machine learning model evaluations, and SHAP analysis. State the implications of the results and their relevance to predicting student graduation. Highlight the importance of machine learning in solving real-world problems and the insights gained through SHAP analysis.
Note: The project places a significant emphasis on leveraging machine learning techniques and utilizing SHAP analysis to enhance interpretability. The combination of these approaches ensures a comprehensive understanding of the data and the model's decision-making process.
Feel Free to modify the Code with new Algorithms
Author Muzammil Mushtaq