https://github.com/abhinavsharma07/kaggle-comp.

A repository showcasing solutions to Kaggle competitions with end-to-end workflows in machine learning and data science.
https://github.com/abhinavsharma07/kaggle-comp.

data-preprocessing datascience deeplearning feature-engineering kaggle machinelearning model-evaluation model-training predictive-modeling

Last synced: 6 months ago
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A repository showcasing solutions to Kaggle competitions with end-to-end workflows in machine learning and data science.

• Host: GitHub
• URL: https://github.com/abhinavsharma07/kaggle-comp.
• Owner: AbhinavSharma07
• License: mit
• Created: 2024-12-05T19:17:21.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2025-04-02T14:20:05.000Z (6 months ago)
• Last Synced: 2025-04-02T15:23:35.533Z (6 months ago)
• Topics: data-preprocessing, datascience, deeplearning, feature-engineering, kaggle, machinelearning, model-evaluation, model-training, predictive-modeling
• Language: Jupyter Notebook
• Homepage: https://www.kaggle.com/
• Size: 31.8 MB
• Stars: 2
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# 🚀 **Kaggle-Comp.**   

![1634020978101](https://github.com/serkannpolatt/KAGGLE-COMPETITION-NOTEBOOKS/assets/92849974/61495376-53b6-4fdc-82c2-85f4efc46998)

             

Welcome to **Kaggle-Comp.**, a dynamic repository showcasing solutions to various **Kaggle competitions**! 🏆  

Your one-stop guide to exploring:  

- 💡 **Machine Learning Techniques**  

- 🛠️ **Data Preprocessing**  

- 🔍 **Feature Engineering**  

- 📈 **Model Evaluation**  

Dive in to discover **end-to-end workflows** crafted to tackle diverse data science challenges. 🌟  

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## 📂 **Repository Structure**  

The repository is neatly organized for seamless navigation. Each competition is housed in its own folder, containing:  

### 📒 **Notebooks**  

- 📊 **Data Exploration (EDA)**  

- 🛠️ **Feature Engineering**  

- 🤖 **Model Training & Evaluation**  

### ⚙️ **Scripts**  

- 🔄 **Automation for Data Preprocessing**  

- 🤝 **Model Training Pipelines**  

- 📤 **Prediction Generation**  

### 📄 **Submission Files**  

- ✅ **Final outputs formatted for Kaggle submission**  

### 📝 **Documentation**  

- 📘 **Readme files or additional notes explaining the approach**  

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