Diamond Price Prediction Analysis Summary
- Data Exploration:
The initial dataset consisted of features such as carat, cut, color, clarity, depth, table, and dimensions (x, y, z). The target variable was the diamond price.
- Data Cleaning:
The dataset was clean with no missing values or anomalies.
- Exploratory Data Analysis (EDA):
Conducted EDA to understand the distribution of key variables and identify potential patterns. Visualized relationships between carat and price.
- Initial Modeling:
Applied a linear regression model initially. Encountered challenges due to categorical features like cut, color, and clarity.
- Feature Engineering:
Transformed categorical features into numerical representations. Implemented Polynomial Regression to capture non-linear relationships.
- Random Forest Model:
Utilized Random Forest Regression for improved predictive performance. Achieved an R-squared value of approximately 0.87. Explored feature importances to understand influential factors.
- Model Evaluation:
Continuously evaluated the model using metrics such as Mean Squared Error, Mean Absolute Error, and R-squared. Observed improvements with the Random Forest model. 8. XGBoost Model:
Experimented with XGBoost, another ensemble method. Achieved comparable results to Random Forest.
- Ensemble Approach:
Combined predictions from Random Forest and XGBoost in an ensemble. Further improved predictive performance.
- Hyperparameter Tuning:
Conducted hyperparameter tuning for both Random Forest and XGBoost models. Explored different parameter combinations for optimal results.
- Residual Analysis:
Analyzed residuals to identify any patterns or systematic errors in model predictions.
- Scaling Features:
Explored the impact of feature scaling, particularly relevant for certain algorithms like Support Vector Regression.
- Cross-Validation:
Utilized cross-validation to ensure model generalization to unseen data.
- Conclusion:
The Random Forest and XGBoost models, individually and in ensemble, demonstrated strong predictive capabilities. The models explain a significant portion of the variance in diamond prices. Further improvements could be explored through continuous experimentation with different algorithms, feature engineering, and hyperparameter tuning.
- Future Directions:
Consider exploring additional features or external datasets that may contribute valuable information. Keep refining and experimenting with different models to find the most suitable for the dataset.