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https://github.com/aelluminate-campus/student-performance-analysis

This project analyzes student data to understand factors affecting final exam scores. Data includes study habits, extracurriculars, family background, school environment, and demographics. The goal is to identify key contributors to academic success.
https://github.com/aelluminate-campus/student-performance-analysis

data-science data-visualization exploratory-data-analysis jupyter-notebook python3

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This project analyzes student data to understand factors affecting final exam scores. Data includes study habits, extracurriculars, family background, school environment, and demographics. The goal is to identify key contributors to academic success.

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# Student Performance Analysis



This project aims to explore the various factors influencing student performance by analyzing a dataset that captures key aspects of students' academic and personal life. The dataset includes information such as study habits (hours studied, tutoring sessions), extracurricular participation, parental involvement, family income, and sleep patterns. Additionally, it incorporates data on school-related factors like teacher quality, peer influence, and school type, along with demographic details like gender and parental education level. By conducting exploratory data analysis, the goal is to uncover relationships between these variables and students' final exam scores, shedding light on the most impactful contributors to academic success.



## Objectives



1. **Data Exploration**: Understand the structure and contents of the dataset, identifying key variables and their distributions.

2. **Data Cleaning**: Handle missing values, outliers, and inconsistencies in the data to ensure its quality and reliability.

3. **Visual Analysis**: Create visualizations to explore relationships between different variables and their impact on student performance.



## The Data



This dataset provides a comprehensive overview of various factors affecting student performance in exams. It includes information on study habits, attendance, parental involvement, and other aspects influencing academic success.



The dataset contains the following columns:



- `Hours_Studied`: Number of hours spent studying per week.

- `Attendance`:	Percentage of classes attended.

- `Parental_Involvement`:	Level of parental involvement in the student's education (Low, Medium, High).

- `Access_to_Resources`: Availability of educational resources (Low, Medium, High).

- `Extracurricular_Activities`:	Participation in extracurricular activities (Yes, No).

- `Sleep_Hours`: Average number of hours of sleep per night.

- `Previous_Scores`:	Scores from previous exams.

- `Motivation_Level`:	Student's level of motivation (Low, Medium, High).

- `Internet_Access`:	Availability of internet access (Yes, No).

- `Tutoring_Sessions`: Number of tutoring sessions attended per month.

- `Family_Income`:	Family income level (Low, Medium, High).

- `Teacher_Quality`: 	Quality of the teachers (Low, Medium, High).

- `School_Type`:	Type of school attended (Public, Private).

- `Peer_Influence`:	Influence of peers on academic performance (Positive, Neutral, Negative).

- `Physical_Activity`:	Average number of hours of physical activity per week.

- `Learning_Disabilities`:	Presence of learning disabilities (Yes, No).

- `Parental_Education_Level`:	Highest education level of parents (High School, College, Postgraduate).

- `Distance_from_Home`:	Distance from home to school (Near, Moderate, Far).

- `Gender`:	Gender of the student (Male, Female).

- `Exam_Score`:	Final exam score.



## Methodology



The analysis will be conducted in Python using popular data science libraries such as **Pandas**, **NumPy**, **Seaborn**, and **Matplotlib**. The steps involved in the analysis include:



- **Data Loading**: Load the dataset into a Pandas DataFrame for further processing.

- **Data Exploration**: Understand the structure and contents of the dataset, identifying key variables and their distributions.

- **Exploratory Data Analysis (EDA)**: Analyze the relationships between different variables and their impact on student performance.

- **Data Visualization**: Create visualizations to represent the data and explore patterns and trends.

  - **Bivariate Analysis**: Explore relationships between pairs of variables to identify potential correlations.

  - **Multivariate Analysis**: Analyze interactions between multiple variables to uncover complex relationships.



## Visualizations



For visualization, you can refer to this [link](https://learn.aelluminate.com/projects/data-science/student-performance-analysis).



## Results



The analysis will provide insights into the factors that most strongly influence student performance, highlighting the key drivers of academic success. By visualizing the relationships between different variables and their impact on exam scores, we aim to identify patterns and trends that can inform strategies for improving student outcomes.



## Conclusion



The findings of this analysis will shed light on the various factors that contribute to student performance, helping educators, policymakers, and parents understand the key drivers of academic success. By identifying the most impactful variables and their relationships, we can develop targeted interventions and support mechanisms to enhance student outcomes and promote a culture of learning and achievement.