Diamond Quality Predictions
The diamond quality prediction project aimed to develop a machine learning model using Python to accurately classify diamonds into quality categories, namely ideal, premium, good, very good, or fair. The model utilized various features such as carat weight, cut, color, clarity, and depth to make predictions. Evaluation metrics like accuracy, precision, recall, and F1 score were employed to assess the model's performance, ensuring its effectiveness in determining diamond quality.
To gain a comprehensive understanding of the dataset, visualizations were created to analyze the distribution of diamond prices, color frequencies, and clarity levels. The price distribution graph provided insights into the range and spread of diamond prices, helping identify any anomalies or patterns. Additionally, a log_price distribution graph was generated to visualize the logarithmic transformation of diamond prices, aiding in detecting non-linear relationships and extreme values.
The color distribution was explored through a count plot, which depicted the number of diamonds in each color category. This visualization allowed for a comparison of color prevalence and offered valuable insights into the popularity of different diamond colors within the dataset. The relationship between color and quality was further examined using a grouped bar chart, enabling the identification of any associations or trends between color and the various quality categories.Similarly, the clarity distribution was analyzed using a grouped bar chart, displaying the distribution of different clarity categories within each quality category. This analysis provided valuable insights into the impact of clarity on the classification of diamond quality.
Finally, the overall distribution of diamond quality categories was examined through a bar chart, presenting the count or frequency of diamonds in each quality category. This visualization offered a concise overview of the proportion of diamonds falling into different quality categories, aiding in decision-making and strategy formulation.In summary, the diamond quality prediction project incorporated machine learning techniques, data visualization, and evaluation metrics to accurately classify diamonds into quality categories. The visualizations provided a comprehensive understanding of the dataset, offering insights into the price, color, and clarity distributions. These findings can guide diamond industry professionals in making informed decisions related to pricing, color preferences, and quality assessment.
Link For the project is given below