Nov '25 • General discussion
Data Analytics Project : 3 💎 Python Data Analysis Project – Diamond Dataset Exploration
Data Analytics Project : 3
💎 Python Data Analysis Project – Diamond Dataset Exploration
I’m pleased to share my first Python Data Analysis Project, where I performed an in-depth analytical study of the Diamonds Dataset, consisting of 53,940 records and 10 features. The primary goal was to understand key factors influencing diamond pricing and quality perception using statistical analysis, visualization, and correlation modeling.
The dataset included numerical and categorical attributes such as carat, cut, color, clarity, depth, table, and price. Using pandas for data manipulation and NumPy for numerical computation, I confirmed data completeness and identified strong relationships between features. A correlation coefficient of 0.92 between carat and price indicated that diamond weight is the dominant price driver, while ANOVA results (F-stat = 324.56, p < 0.001) confirmed significant price differences across cut categories.
Further analysis revealed that the 0.9–1.1 carat range provides the best price-to-value ratio, with VS2 clarity and Very Good cut combinations performing optimally. Visual insights created through Matplotlib and Seaborn illustrated right-skewed price distribution and clear quality segmentation. These findings supported strategic recommendations around inventory optimization, segment-based pricing, and premium positioning.
This project strengthened my skills in exploratory data analysis (EDA), hypothesis testing, data visualization, and statistical interpretation. It also provided real-world business insights, demonstrating how analytical reasoning and Python-based data workflows can guide data-driven decision-making in pricing and market strategy.
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Data Analytics Project : 3 💎 Python Data Analysis Project – Diamond Dataset Exploration
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