Student Performance in Exams Dataset Analysis
Overview This repository contains an analysis of a dataset on the performance of students in exams. The dataset explores various factors that may influence students' academic outcomes, including demographic information, parental background, test preparation, and final exam scores.
Dataset Information Format: CSV Columns: gender: Student's gender (e.g., male or female) race/ethnicity: Ethnicity or race group of the student parental level of education: Highest level of education attained by the student's parents lunch: Type of lunch the student receives (e.g., standard or free/reduced) test preparation course: Whether the student completed a test preparation course (yes or no) math score: Student's score in the math exam reading score: Student's score in the reading exam writing score: Student's score in the writing exam Analysis The analysis is performed using Jupyter Notebooks and includes the following steps:
Data Exploration: Initial exploration of the dataset to understand its structure and characteristics. Data Cleaning: Handling missing values, checking for outliers, and ensuring data integrity. Descriptive Statistics: Calculating summary statistics to gain insights into the overall performance of students. Visualization: Creating visualizations (e.g., histograms, bar plots) to illustrate patterns and relationships in the data. Inferential Statistics: Conducting statistical tests or analyses to draw conclusions about the dataset. Conclusions: Summarizing key findings and insights from the analysis. Usage To replicate or extend the analysis, follow these steps:
Clone the repository to your local machine. Download the dataset from the provided Kaggle link and place it in the data/ directory. Open and run the Jupyter Notebooks in the notebooks/ directory in sequential order. Feel free to explore and contribute to the analysis! If you have any questions or suggestions, please open an issue or submit a pull request.
Acknowledgments The dataset used in this analysis is sourced from Kaggle. Inspiration for analysis and visualizations from the data science community.