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Student Spending Analysis in Python
Student Spending Analysis in Python
This data exploration project aims to provide insights into the spending habits of college students. By analyzing various aspects of spending data, I sought to identify trends, make data-driven recommendations, and gain a deeper understanding of student's spending habits.
Tools: Python, Jupytor Notebook
Libraries: NumPy, Pandas, Matplotlib, Seaborn
Data Source: Student Spending Data: The primary dataset used for this analysis is the "student_spending.xlsx" file containing detailed information about student spending. This data was obtained on Kaggle.com.
Data Cleaning
Remove Duplicates: Identified and removed duplicate records to ensure the dataset's integrity and accuracy.
Define Numeric and Non-Numeric Features: Categorized features into numeric and non-numeric types to facilitate appropriate analysis and processing.
Analyze Missing Values: Calculated the percentage of missing values for each feature to determine the extent of data completeness.
Handle Null Values: Employed imputation techniques to address and fill in missing values, ensuring a complete dataset for analysis.
Identify and Remove Outliers: Detected and removed outliers to minimize their impact on the data analysis and ensure the accuracy of insights.
Data Analysis and Visualization
Data Analysis and Visualization
Statistical Analysis
Statistical Analysis
Conducted a detailed statistical analysis to gain insights into students' spending habits in relation to gender, school year, and income level.
Calculated key statistical metrics, including mean, median, and standard deviation, to summarize and understand the data distribution and variability.
Data Visualization
Data Visualization
Developed a series of visualizations to effectively communicate the findings:
Bar Plots and Bar Graphs: Utilized Matplotlib's pyplot and Seaborn to create bar plots and bar graphs, highlighting trends and comparisons between different categories.
Pie Charts: Employed pie charts to illustrate the proportional distribution of spending categories or demographic segments.
Insights and Recommendations
Insights and Recommendations
Analysis of Monthly Expenses
Analysis of Monthly Expenses
Housing is the highest monthly expenditure on average for students by a wide margin, with food costs being the second highest expenditure, and technology and transportation close to being tied for the third largest expenditure.
Recommendation: Develop strategies to assist students in managing these expenses effectively, such as providing resources for affordable housing options, meal planning tips, and transportation subsidies or alternatives.
Expenditures by School Year
Expenditures by School Year
Overall expenditures do not show significant differences across grade levels. However, a notable trend emerges where seniors tend to incur slightly higher housing costs, while freshmen allocate more funds toward school supplies compared to other school years.
Monthly Expenditures by Gender
Monthly Expenditures by Gender
On average, males spend slightly more on housing compared to female and non-binary students. However, spending patterns in other categories remain relatively consistent across all genders, indicating that housing is a distinct area of expenditure where gender differences are more pronounced.
Monthly Income Analysis
Monthly Income Analysis
The analysis indicates a relatively even distribution of monthly income across both genders and different years of schooling. This suggests a consistent earning potential among students regardless of gender or academic progress.
Spending Data Distribution
Spending Data Distribution
The spending data across gender and school year is evenly represented, suggesting that the dataset is well-balanced and provides a reliable basis for analysis.
These findings highlight opportunities to support students in optimizing their financial well-being and guidance on managing major expenditures effectively.
Challenges
Challenges
In the initial phase of this project, I used manual coding techniques. To improve efficiency and reduce errors, I revisited the code and restructured it to be fully automated. This transition to automated code enhances efficiency, minimizes errors, and facilitates smoother future use.
Initially, my bar graphs, which aimed to display average monthly costs segmented by gender and year in school, did not accurately reflect the information. The primary issue was that the data was stored in a dictionary format, which required conversion to a DataFrame for effective plotting. Additionally, I realized that the plotting code was incomplete. To resolve these issues, I utilized ChatGPT for troubleshooting and guidance.
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