Descriptive statistics provide a fundamental summary of datasets through measures such as mean, median, mode, standard deviation, variance, range, minimum, and maximum. This project aimed to extract meaningful insights from raw datasets in agriculture, education, and health by applying statistical summaries that describe data behavior before moving into more complex analysis or modeling.
Using Python (Pandas, NumPy) and Excel, I cleaned and prepared datasets, then applied statistical functions to compute central tendencies and dispersion metrics. I also used visualizations such as histograms, boxplots, and summary tables to illustrate data distributions and detect outliers. The analysis was conducted on several domains, including student exam results, soil nutrient contents, and crop productivity records.
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In a soil nutrient dataset, the standard deviation of nitrogen levels was high, indicating inconsistency across plots—helping advise better fertilizer targeting.
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In student assessment data, mean scores and interquartile ranges highlighted performance gaps and supported customized intervention.
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These descriptive metrics laid the foundation for further statistical testing (e.g., T-tests, ANOVA) and machine learning inputs.
- Descriptive Summary (Mean, Median, SD, Variance, IQR)
- Python (Pandas, NumPy, Seaborn, Matplotlib)
- Outlier Detection and Data Distribution Analysis
- Data Cleaning and Reporting
- Application across Agriculture, Education, and Health Sectors