Exercise Set

Welcome to the Exercise Set! This repository contains a collection of programming exercises aimed at improving your data analysis and machine learning skills. The exercises are implemented in Jupyter notebooks and cover different datasets and techniques.

Repository Structure

├── README.md
├── glass-imbalanced.ipynb
├── hotel-reservations.ipynb
└── skillmaster.ipynb

1. Glass Imbalanced Dataset Analysis (glass-imbalanced.ipynb)

This notebook focuses on analyzing the Glass Imbalanced dataset. It includes:

• Data loading and preprocessing
• Handling missing values
• Exploratory Data Analysis (EDA) using seaborn and matplotlib
• Distribution visualization of refractive index by class

2. Hotel Reservations Data Analysis (hotel-reservations.ipynb)

This notebook processes and analyzes hotel booking data. It includes:

• Data preprocessing (handling missing values, encoding categorical variables)
• Exploratory Data Analysis (EDA)
• Logistic Regression for predicting booking cancellations
• Performance evaluation using confusion matrix and ROC curve

3. SkillMaster - Udemy Course Analysis (skillmaster.ipynb)

This notebook analyzes Udemy course data. It covers:

• Data cleaning and feature engineering
• Encoding categorical variables
• Classification using K-Nearest Neighbors (KNN)
• Model evaluation using accuracy metrics and classification reports

Requirements

To run the notebooks, ensure you have the following dependencies installed:

pip install numpy pandas seaborn matplotlib scikit-learn

Usage

1. Clone the repository:

   git clone https://github.com/salimawad85/git-kaggle.git

2. Navigate to the directory:

   cd salimawad85-git-kaggle

3. Open Jupyter Notebook:

   jupyter notebook

4. Run the notebooks interactively.

Contributing

Feel free to contribute by submitting pull requests or reporting issues.

License

This project is open-source and available under the MIT License.