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https://github.com/chinmayrane16/diamonds-in-depth-analysis

Given dataset of Diamonds with features such as Cut, Carat, Clarity etc. I have used libraries such as Pandas, Numpy, Matplotlib, Seaborn to Analyse and Estimate the Price of Diamonds based on the features. Using Scikit-Learn , implemented Algorithms to increase the effective R2 score.
https://github.com/chinmayrane16/diamonds-in-depth-analysis

data-visualization feature-engineering feature-scaling machine-learning matplotlib numpy pandas seaborn

Last synced: 10 days ago
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Host: GitHub
URL: https://github.com/chinmayrane16/diamonds-in-depth-analysis
Owner: Chinmayrane16
License: mit
Created: 2018-07-10T06:22:47.000Z (over 6 years ago)
Default Branch: master
Last Pushed: 2018-08-22T15:20:34.000Z (about 6 years ago)
Last Synced: 2024-10-10T19:10:14.389Z (27 days ago)
Topics: data-visualization, feature-engineering, feature-scaling, machine-learning, matplotlib, numpy, pandas, seaborn
Language: Jupyter Notebook
Homepage:
Size: 1.94 MB
Stars: 18
Watchers: 1
Forks: 6
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

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README

# Diamonds-In-Depth-Analysis
**Diamonds**
* Given dataset of Diamonds with features such as Cut, Carat, Clarity etc.
* I have used Libraries such as _Pandas, Numpy, Matplotlib, Seaborn_ to Analyse and Estimate the **Price of Diamonds** based on the features.
* Used _Scikit-Learn_ to implement Regression models to improve the **R2 Score**.
* Analyzed and Visualized both the distribution of Categorical and Numerical Features.
* Used StandardScaler to Scale the numerical values.
* Finally, I have Tuned the Parameters with the help of _GridSearchCV_.