Machine Learning Project

Diamond price prediction is the process of estimating the value or cost of a diamond based on various factors and characteristics. It involves using data analysis, statistical modeling, and machine learning techniques to predict the market price or worth of a diamond.

Diamonds are precious gemstones that are evaluated based on their unique features, known as the "Four Cs": carat, cut, color, and clarity. These factors, along with additional aspects such as depth and table, play a crucial role in determining a diamond's value.

Requirements

• sklearn
• pandas
• flask
• seaborn
• python

Installation and Usage

To install webApp, follow these steps:

Environment Setup

conda create -p venv python==3.8

conda activate venv/

1. Clone the repository:

git clone https://github.com/ankitrajput77/DiamondPricePrediction.git

2. Navigate to the project directory:

cd DiamondPricePrediction

3. Install dependencies:

pip install -r requirements.txt

4. Set up :

python application.py

5. Start the server:

http://127.0.0.1:5000/

Notebook

Used dataset is present on kaggle

• Use this notebook to access : Kaggle Notebook
• LinkedIn Post of Notebook (you can download notebook pdf) : LinkedIn Post

In Details

├──  artifacts                    - here's the model pickle and dataset files.
│    └── preprocessor.pkl  
│    └── model.pkl
│    └── raw.csv
│    └── train.csv
│    └── test.csv
│
│
├──  Logs  
│    └── time_format.log          - here's the specific run log files.
│ 
│
├──  notebooks  
│    └── EDA.ipynb                        - here's the EDA notebook.
│    └── Model-training.ipynb             - here's the model training notebook.
│    └── data 		                        - here's the folder for dataset.
│          └── gemstone.csv               - here's the dataset present about diamond details.
│
│
├──  prediction_tries
│   ├── prediction.py                     - code to predict the price for test_data.
│   └── test_data.csv                     - here's the test_data.
│   └── test_pred.csv                     - here's the predicted values for test_data(it will generate after running prediction.py).
│
│
│
├── src                                   - The "src" folder, short for "source".
│   └── exception.py                      - Exception handling.
│   └── logger.py                         - log file handling.
│   └── utils.py                          - util functions.
│   └── components
│          └── data_ingestion.py          - code for data ingestion.
│          └── data_transformation.py     - code for data transformation.
│          └── model_trainer.py           - code for model training.
│   └── pipelines
│          └── prediction_pipeline.py     - code for model prediction 
│          └── training_pipeline.py       - code for training of model 
│
│
│
├── static                                - this folder contains frontend css files.
│   └── css
│        └── styles.css 
│   └── images
│        └── github_logo
│        └── kaggle_logo
│
│
├── templates                             - this folder contains html files.
│   └── home.html
│   
│ 
│ 
├── application.py                        - Code for webapp running.
│					
└──setup.py                               - project's metadata and configuration details

InAction

Contributing

Any kind of enhancement or contribution is welcomed.

Contact

If you have any questions, feedback, or suggestions, feel free to reach out to us at [email protected].