Gemstone Price Prediction - Utkarsh Gaikwad

Introduction About the Data :

The dataset The goal is to predict price of given diamond (Regression Analysis).

There are 10 independent variables (including id):

• id : unique identifier of each diamond
• carat : Carat (ct.) refers to the unique unit of weight measurement used exclusively to weigh gemstones and diamonds.
• cut : Quality of Diamond Cut
• color : Color of Diamond
• clarity : Diamond clarity is a measure of the purity and rarity of the stone, graded by the visibility of these characteristics under 10-power magnification.
• depth : The depth of diamond is its height (in millimeters) measured from the culet (bottom tip) to the table (flat, top surface)
• table : A diamond's table is the facet which can be seen when the stone is viewed face up.
• x : Diamond X dimension
• y : Diamond Y dimension
• x : Diamond Z dimension

Target variable:

• price: Price of the given Diamond.

Dataset Source Link : https://www.kaggle.com/competitions/playground-series-s3e8/data?select=train.csv

It is observed that the categorical variables 'cut', 'color' and 'clarity' are ordinal in nature

Check this link for details : American Gem Society

AWS Deployment Link :

AWS Elastic Beanstalk link : http://gemstonepriceutkarshgaikwad-env.eba-7zp3wapg.ap-south-1.elasticbeanstalk.com/

Screenshot of UI

YouTube Video Link

Link for YouTube Video : Click the below thumbnail to open

AWS API Link

API Link : http://gemstonepriceutkarshgaikwad-env.eba-7zp3wapg.ap-south-1.elasticbeanstalk.com/predictAPI

Postman Testing of API :

Approach for the project

1. Data Ingestion :

   • In Data Ingestion phase the data is first read as csv.
   • Then the data is split into training and testing and saved as csv file.

2. Data Transformation :

   • In this phase a ColumnTransformer Pipeline is created.
   • for Numeric Variables first SimpleImputer is applied with strategy median , then Standard Scaling is performed on numeric data.
   • for Categorical Variables SimpleImputer is applied with most frequent strategy, then ordinal encoding performed , after this data is scaled with Standard Scaler.
   • This preprocessor is saved as pickle file.

3. Model Training :

   • In this phase base model is tested . The best model found was catboost regressor.
   • After this hyperparameter tuning is performed on catboost and knn model.
   • A final VotingRegressor is created which will combine prediction of catboost, xgboost and knn models.
   • This model is saved as pickle file.

4. Prediction Pipeline :

   • This pipeline converts given data into dataframe and has various functions to load pickle files and predict the final results in python.

5. Flask App creation :

   • Flask app is created with User Interface to predict the gemstone prices inside a Web Application.

Exploratory Data Analysis Notebook

Link : EDA Notebook

Model Training Approach Notebook

Link : Model Training Notebook

Model Interpretation with LIME

Link : LIME Interpretation