This repository contains assignments and quizzes from the Data Mining subject in Faculty of Computer and Information Science. It includes practical tasks designed to build skills in data preprocessing, analysis, visualization, and machine learning.
This folder contains smaller exercises aimed at testing foundational concepts in data mining.
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Quiz 1:
- Task: Analyze employee data and visualize salary distributions.
- Technologies:
pandas,seaborn,matplotlib
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Quiz 2:
- Task: Perform association rule mining using the Apriori algorithm.
- Technologies:
mlxtend,pandas
This folder contains detailed assignments focusing on a variety of data mining techniques.
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Assignment 1:
- Task: Receive user inputs and calculate grades based on predefined criteria.
- Technologies: Python input/output
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Assignment 2:
- Task: Calculate and interpret Body Mass Index (BMI) values.
- Technologies: Python arithmetic and conditional logic
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Assignment 3:
- Task: Apply operations like sorting, filtering, and sampling on a dataset.
- Technologies:
pandas
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Assignment 4:
- Task: Train machine learning models (Decision Tree, K-Nearest Neighbors, Naive Bayes) to classify data.
- Dataset:
cars.csv - Technologies:
pandas,numpy,scikit-learn
Ensure you have Python 3.x installed along with the following libraries:
pandasnumpymatplotlibseabornmlxtendscikit-learn
These assignments aim to enhance understanding and application of data mining concepts, including:
- Data filtering and visualization
- Association rule mining
- DataFrame operations
- Machine learning model implementation
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Clone this repository:
git clone https://github.com/MoSalem149/data-mining-assignments.git cd data-mining-assignments -
Install required Python packages:
pip install -r requirements.txt
To run any script, navigate to the respective folder (quizzes or tasks) and execute the file:
python <script_name>.pyFor Jupyter Notebooks:
jupyter notebook <notebook_name>.ipynb- Mohamed Salem - MoSalem149