📊 Data Science Projects

Welcome to my Data Science project repository! This repo serves as a collection of hands-on projects I've built across various areas of data science, including web scraping, data visualization, and machine learning.

✅ Currently included projects:

• 🎬 film-scraping-analysis: Python web scraping and visualization project
• 📚 predict-students-academic: Machine learning project to predict student academic performance

More projects will be added continuously, covering areas such as data preprocessing, feature engineering, NLP, and deep learning.

---

🎬 film-scraping-analysis

Overview: This project uses Python to scrape movie information (ratings, number of reviews, release years, etc.) from a film website and visualize the data using popular data science libraries.

Highlights:

• Automated scraping of structured movie data from multiple pages
• Visualization of rating distributions, trends by year, and more
• Clean and readable charts with support for multilingual labels

Tech stack:

• Web scraping: requests, BeautifulSoup
• Data manipulation & visualization: pandas, matplotlib, seaborn

---

📚 predict-students-academic

Overview: This project predicts students' academic performance based on their demographic and behavioral data, using various machine learning models.

Highlights:

• Data exploration and preprocessing (handling missing values, encoding categorical features, etc.)
• Comparison of multiple models (e.g., Logistic Regression, Random Forest, XGBoost)
• Model evaluation using cross-validation, confusion matrix, ROC curves

Tech stack:

• Data handling: pandas, numpy
• Machine learning: scikit-learn, xgboost
• Visualization: matplotlib, seaborn

---

📁 Project Structure

├── film-scraping-analysis/
│   ├── scraper.py
│   ├── analysis.ipynb
│   └── README.md
│
├── predict-students-academic/
│   ├── data/
│   ├── model_training.ipynb
│   └── README.md
│
└── README.md  ← (This file)

---

🚀 Getting Started

1. Clone the repository:

   git clone https://github.com/your-username/your-repo-name.git
   cd your-repo-name

2. (Optional) Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install required packages:

   pip install -r requirements.txt

4. Navigate to each project folder and run the notebooks or scripts as needed.

---

🧠 Tools & Technologies

• Python 3.x
• Jupyter Notebook
• pandas, numpy
• matplotlib, seaborn
• scikit-learn, xgboost
• requests, BeautifulSoup

---

🔭 Future Plans

• Add NLP projects (e.g., sentiment analysis, text classification)
• Incorporate deep learning models (TensorFlow or PyTorch)
• Work with real-world datasets for end-to-end pipelines