This project involves the analysis of student performance using Seaborn plots in Jupyter Notebook. The dataset contains information about students' demographics, study habits, and performance in various subjects. Through this analysis, we aim to gain insights into the factors that influence student performance and visualize the relationships between different variables.
The dataset used for this analysis includes information about students' attributes such as gender, parental education level, test preparation course, and scores in math, reading, and writing.
Before running the code, make sure you have the following dependencies installed:
- Python (3.x)
- Jupyter Notebook
- Pandas
- NumPy
- Matplotlib
- Seaborn
To get started, follow the steps below:
- Clone the repository:
git clone https://github.com/shaadclt/Student-Performance-Analysis.git- Change into the project directory:
cd Student-Performance-Analysis-
Install the required dependencies:
-
Run Jupyter Notebook:
jupyter notebook-
Open the
Student Performance Analysis.ipynbnotebook in Jupyter. -
Run the notebook cells to load the dataset, perform the analysis, and generate Seaborn plots.
The notebook provides a step-by-step guide to analyze student performance. The analysis includes the following tasks:
- Loading and understanding the dataset
- Data cleaning and preprocessing
- Exploratory Data Analysis (EDA) using descriptive statistics
- Visualizing the distributions of scores using Seaborn plots
- Analyzing the relationships between different variables using scatter plots, bar plots, and other Seaborn visualizations
- Drawing insights and conclusions based on the analysis results
Throughout the analysis, various Seaborn plots such as histograms, box plots, violin plots, and scatter plots are used to visualize the student performance and relationships between different variables. These visualizations provide insights into factors that may influence student scores, such as parental education level, test preparation, or study time.
The notebook also includes interpretations and conclusions based on the analysis results. Feel free to refer to the notebook for detailed insights.
You can customize the analysis to suit your specific requirements. For example, you can explore additional variables, create new visualizations using Seaborn's extensive plotting capabilities, or apply different statistical techniques to gain deeper insights into student performance.
This project is licensed under the MIT License. See the LICENSE file for more information.
- This analysis is inspired by the desire to understand the factors that influence student performance and help identify potential areas for improvement.
Contributions are welcome! If you find any issues or have suggestions for improvements, please open an issue or submit a pull request.