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https://github.com/cyberfantics/data-mining
https://github.com/cyberfantics/data-mining
Last synced: about 1 month ago
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- Host: GitHub
- URL: https://github.com/cyberfantics/data-mining
- Owner: cyberfantics
- Created: 2024-11-12T04:42:08.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2024-11-12T04:49:26.000Z (about 2 months ago)
- Last Synced: 2024-11-12T05:28:09.576Z (about 2 months ago)
- Language: Jupyter Notebook
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Data Mining Course Repository
Welcome to the **Data Mining Course Repository** for BS 7. This repository contains all resources, code, and assignments related to the Data Mining course, aimed at building a strong foundation in data analysis and knowledge discovery.
## Course Overview
Data mining involves extracting valuable insights from large datasets, uncovering patterns, and supporting decision-making processes. This course covers fundamental techniques, algorithms, and tools used in data mining, including preprocessing, classification, clustering, association analysis, and more.
## Repository Structure
The repository is organized as follows:
- **Lecture Notes**: Contains lecture slides, notes, and references.
- **Assignments**: Includes all assignments given during the course, along with solutions.
- **Projects**: Holds project files and code relevant to various data mining tasks.
- **Datasets**: Provides datasets used for exercises, assignments, and projects.
## Course Topics
The following topics are covered in this course:
1. **Data Preprocessing**: Cleaning and preparing data for analysis.
2. **Classification**: Algorithms for categorizing data, including decision trees, Naive Bayes, and k-NN.
3. **Clustering**: Techniques for grouping data, such as k-means and hierarchical clustering.
4. **Association Rule Mining**: Discovering relationships between variables in large datasets.
5. **Dimensionality Reduction**: Reducing data complexity for visualization and processing.
6. **Evaluation Metrics**: Techniques to assess model performance.
## Getting Started
To get started, clone this repository:
```
git clone https://github.com/cyberfantics/data-mining.git
```
## Prerequisites
- Python 3.x
- **Libraries:** numpy, pandas, matplotlib, scikit-learn
## Install required libraries with:
```
pip install -r requirements.txt
```
### How to Use This Repository
- Read through lecture notes to understand the concepts.
- Complete the assignments by following the provided instructions.
- Experiment with datasets and try out different data mining techniques.
### Contributing
- If you have suggestions or improvements, feel free to submit a pull request. Contributions are welcome!
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