https://github.com/evan-dg31/data-science

Exploratory Data Analysis (EDA), Predictive Modeling (Supervised and Unsupervised), Regression, Classification, Clustering
https://github.com/evan-dg31/data-science

classification clustering data-analysis data-science data-visualization machine-learning matplotlib numpy pandas python regression-analysis seaborn

Last synced: about 1 month ago
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Exploratory Data Analysis (EDA), Predictive Modeling (Supervised and Unsupervised), Regression, Classification, Clustering

• Host: GitHub
• URL: https://github.com/evan-dg31/data-science
• Owner: evan-dg31
• Created: 2025-02-24T06:19:33.000Z (12 months ago)
• Default Branch: main
• Last Pushed: 2025-02-24T07:31:45.000Z (12 months ago)
• Last Synced: 2025-02-24T07:34:54.975Z (12 months ago)
• Topics: classification, clustering, data-analysis, data-science, data-visualization, machine-learning, matplotlib, numpy, pandas, python, regression-analysis, seaborn
• Language: Jupyter Notebook
• Homepage:
• Size: 1.23 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
![Image](https://github.com/user-attachments/assets/8653788b-e09a-44c1-89a6-34a776cdb015)

# Data-Science

This repository contains a data science project focused on analyzing and modeling data to gain insights and make predictions.

1. [Exploratory Data Analysis (EDA)](https://github.com/evan-dg31/Data-Science/tree/61d823c27159d03c327c95d0695285cfa45d1518/Exploratory%20Data%20Analysis%20(EDA)) - including scoping, data gathering & cleaning, EDA, and feature engineering

2. [Regression Analysis](https://github.com/evan-dg31/Data-Science/tree/62391724f4942933035562a1e8184c60fee908b3/Regressions) - including [Simple Regression](https://github.com/evan-dg31/Data-Science/tree/62391724f4942933035562a1e8184c60fee908b3/Regressions/Simpler%20Regression) and [Multiple Linear Regression](https://github.com/evan-dg31/Data-Science/tree/62391724f4942933035562a1e8184c60fee908b3/Regressions/Multiple%20Linear%20Regression) & regularized regression, forecasting, and validation & testing.

3. Classification Analysis - including KNN, logistic regression, decision trees, random forests, GBMs, XGBoost

4. Cluster Analysis - including clustering, anomaly detection, dimensionality reduction, and recommenders

5. Advance Data Science - including NLP, Deep Learning, RNN, LSTM, GRU RNN

Each project has more detailed documentation tht explains the summary and key takeaways of the project.