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Predictive Modeling Project
Diamond Price Prediction
Chirag Ghelani
Abdelhamed Hamed
Srikruthi Jonnavithula
Malgorzata Pozniak
Zhonghua Zhang
➢ Project Goal
➢ Dataset
➢ Data Pre-processing
➢ Descriptive Analytics
➢ Predictive Analytics
➢ Model Refinement
➢ Conclusions and Recommendations
Agenda
The goal of the project is to build a model that can
accurately predict the price of a diamond given its
weight, quality and dimensional measurements.
Project Goal
➢ Dataset downloaded from Kaggle.com
➢ Prices and other attributes of 53920 diamonds
➢ 10 columns
Dataset
GIA Report
➢ Data Type Verification
➢ Outliers Resolution
➢ Missing Values
Data Pre-processing
Data Type Verification
➢ Cut, color and clarity as nominal data type
➢ Changed them to ordinal as quality has natural order
➢ To create indicator columns as needed
➢ Identify potential outliers using Explore
Outliers tool
➢ Manually resolve the outliers based on
business knowledge
Outlier Resolution
Not real outliers:
Potential outliers:
Real outliers:
Missing Values
➢ 20 rows with missing data
➢ not considered large number
➢ Deleted those 20 rows
➢ A strong positive correlation between clarity, carat size and price
Descriptive Analytics
Price
Clarity
Carat
➢ Majority of high price diamonds bought are of average clarity but having bigger carat
size
Descriptive Analytics
Price Carat
Cut
Clarity
➢ The dataset contains higher concentration of 4 clarity diamonds i.e. Sl1,Sl2, VS1 & VS2
➢ Ideal , Premium & Very Good cut have a major share
Descriptive Analytics
Clarity
Cut
Count
➢ Fair cut category has few high clarity diamonds
Descriptive Analytics
Clarity
Carat
Price
Color
➢ Multivariate Correlations
○ Correlation between numeric
variables
○ Categorical variables not included
Descriptive Analytics
➢ Data Partitioning
➢ Linear Regression (LR) with PCA
➢ Linear Regression
➢ Decision Trees
➢ Neural Networks (NN)
➢ Models (LR & NN) post Cluster Analysis
➢ Ensemble Model with LR and NN
Predictive Analytics
➢ The dataset is partitioned to three subsets
○ Training: 50%
○ Validation: 25%
○ Test: 25%
➢ Models use the same partitioned data when applicable
Data Partition
➢ The first Principal Component covers greater than 98% of the variance
➢ RSquare (0.809) and RMSE (1739.17) are not ideal
➢ Modest results potentially due to not including other predictors (cut, color and clarity)
Linear Regression with PCA
➢ Stepwise Regression is the one where different linear combinations of predictors are
used in each iteration until the stopping rule is met and the metrics like Rsquare, RMSE
is at optimum.
➢ RSquare (0.92) and RMSE (1145.82) are optimal for this model.
➢ Predictor Contribution in the Model
Linear Regression
➢ Decision Tree at 5 splits as the most
optimal solution
➢ Split History
➢ RSquare (0.85) and RMSE (~ 1500)
Decision Trees
➢ Initially used all predictors to gain insight
➢ Two hidden layers and TanH function are
used
➢ RSquare (0.98) and RMSE (576) are the best
results.
Neural Networks
➢ Cluster Analysis before building model
➢ K-means Clustering more suitable
➢ Three cluster solution used for modeling
➢ Linear regression and neural net for each cluster
➢ RSquare (0.90 - 0.98) and RMSE (557- 1259 )
Modeling post Cluster Analysis
➢ Rationale to model refinement
○ Initial neural network models with all predictors showed great promise
○ Simplifying model with less variables and similar results became focus
➢ Steps and Results of model refinement
○ Re-launch the models under different scenarios
○ Carat, cut, color and clarity are the final chosen predictors
○ Best model achieved RSquare of 0.978 and RMSE of 579 - nearly the best
values of all
Model Refinement
Model Refinement: Before & After
Ensemble Model
➢ Ensemble model: Average of Linear Regression and Neural Network
➢ RSquare (0.96) and RASE (787)
➢ Neural Network model still the best
➢ Neural Networks outperformed all other types of model.
➢ A neural network model with 4 c predictors is the most robust.
➢ Carat has the largest effect on price, followed by cut, clarity, and color.
➢ It is recommended businesses or consumers use the neural network
model to determine the price of a diamond.
Conclusions and Recommendations
Questions

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Diamond Price Prediction Models: Neural Network Outperforms with RSquare 0.98

  • 1. Predictive Modeling Project Diamond Price Prediction Chirag Ghelani Abdelhamed Hamed Srikruthi Jonnavithula Malgorzata Pozniak Zhonghua Zhang
  • 2. ➢ Project Goal ➢ Dataset ➢ Data Pre-processing ➢ Descriptive Analytics ➢ Predictive Analytics ➢ Model Refinement ➢ Conclusions and Recommendations Agenda
  • 3. The goal of the project is to build a model that can accurately predict the price of a diamond given its weight, quality and dimensional measurements. Project Goal
  • 4. ➢ Dataset downloaded from Kaggle.com ➢ Prices and other attributes of 53920 diamonds ➢ 10 columns Dataset
  • 6. ➢ Data Type Verification ➢ Outliers Resolution ➢ Missing Values Data Pre-processing
  • 7. Data Type Verification ➢ Cut, color and clarity as nominal data type ➢ Changed them to ordinal as quality has natural order ➢ To create indicator columns as needed
  • 8. ➢ Identify potential outliers using Explore Outliers tool ➢ Manually resolve the outliers based on business knowledge Outlier Resolution Not real outliers: Potential outliers: Real outliers:
  • 9. Missing Values ➢ 20 rows with missing data ➢ not considered large number ➢ Deleted those 20 rows
  • 10. ➢ A strong positive correlation between clarity, carat size and price Descriptive Analytics Price Clarity Carat
  • 11. ➢ Majority of high price diamonds bought are of average clarity but having bigger carat size Descriptive Analytics Price Carat Cut Clarity
  • 12. ➢ The dataset contains higher concentration of 4 clarity diamonds i.e. Sl1,Sl2, VS1 & VS2 ➢ Ideal , Premium & Very Good cut have a major share Descriptive Analytics Clarity Cut Count
  • 13. ➢ Fair cut category has few high clarity diamonds Descriptive Analytics Clarity Carat Price Color
  • 14. ➢ Multivariate Correlations ○ Correlation between numeric variables ○ Categorical variables not included Descriptive Analytics
  • 15. ➢ Data Partitioning ➢ Linear Regression (LR) with PCA ➢ Linear Regression ➢ Decision Trees ➢ Neural Networks (NN) ➢ Models (LR & NN) post Cluster Analysis ➢ Ensemble Model with LR and NN Predictive Analytics
  • 16. ➢ The dataset is partitioned to three subsets ○ Training: 50% ○ Validation: 25% ○ Test: 25% ➢ Models use the same partitioned data when applicable Data Partition
  • 17. ➢ The first Principal Component covers greater than 98% of the variance ➢ RSquare (0.809) and RMSE (1739.17) are not ideal ➢ Modest results potentially due to not including other predictors (cut, color and clarity) Linear Regression with PCA
  • 18. ➢ Stepwise Regression is the one where different linear combinations of predictors are used in each iteration until the stopping rule is met and the metrics like Rsquare, RMSE is at optimum. ➢ RSquare (0.92) and RMSE (1145.82) are optimal for this model. ➢ Predictor Contribution in the Model Linear Regression
  • 19. ➢ Decision Tree at 5 splits as the most optimal solution ➢ Split History ➢ RSquare (0.85) and RMSE (~ 1500) Decision Trees
  • 20. ➢ Initially used all predictors to gain insight ➢ Two hidden layers and TanH function are used ➢ RSquare (0.98) and RMSE (576) are the best results. Neural Networks
  • 21. ➢ Cluster Analysis before building model ➢ K-means Clustering more suitable ➢ Three cluster solution used for modeling ➢ Linear regression and neural net for each cluster ➢ RSquare (0.90 - 0.98) and RMSE (557- 1259 ) Modeling post Cluster Analysis
  • 22. ➢ Rationale to model refinement ○ Initial neural network models with all predictors showed great promise ○ Simplifying model with less variables and similar results became focus ➢ Steps and Results of model refinement ○ Re-launch the models under different scenarios ○ Carat, cut, color and clarity are the final chosen predictors ○ Best model achieved RSquare of 0.978 and RMSE of 579 - nearly the best values of all Model Refinement
  • 24. Ensemble Model ➢ Ensemble model: Average of Linear Regression and Neural Network ➢ RSquare (0.96) and RASE (787) ➢ Neural Network model still the best
  • 25. ➢ Neural Networks outperformed all other types of model. ➢ A neural network model with 4 c predictors is the most robust. ➢ Carat has the largest effect on price, followed by cut, clarity, and color. ➢ It is recommended businesses or consumers use the neural network model to determine the price of a diamond. Conclusions and Recommendations