Data Science and Its Applications (21AD62) - Laboratory Experiments

Welcome to the laboratory experiments for Data Science and Its Applications (Sub Code: 21AD62). This repository provides practical exercises across five modules, covering essential aspects of data science.

📚 Modules Overview

Module 1: Introduction and Basic Data Handling

1. Setup: Install Python/R and configure IDEs (VS Code, PyCharm).
2. Programming: Write and execute basic programs in Python/R.
3. Visualization: Plot a line chart showing study hours vs. exam scores.
4. Histogram: Visualize the frequency distribution of 'mpg' from mtcars.csv.

Module 2: Data Cleaning and Preparation

1. Books Dataset:
   • Import and clean BL-Flickr-Images-Book.csv.
   • Drop irrelevant columns, reindex, and tidy fields.

Module 3: Supervised Learning

1. Logistic Regression: Train a regularized logistic regression on the Iris dataset.
2. SVM Classifier: Experiment with SVM kernels and hyperparameters to find the best accuracy.

Module 4: Unsupervised Learning

1. Decision Trees: Implement ID3 algorithm on a given dataset.
2. Clustering: Apply K-means, Single-link, and Complete-link hierarchical clustering on spiral.txt.

Module 5: Mini Project

1. Web Scraping: Develop a simple web scraping tool for social media data.

📊 Datasets Used

• Mtcars Dataset
• Books Dataset
• Iris Dataset
• Spiral Dataset

🚀 Getting Started

1. Clone the repository:

   git clone https://github.com/yourusername/21AD62-DataScience-Labs.git
   cd 21AD62-DataScience-Labs

2. Install required packages:
   • For Python: pip install matplotlib pip install pandas pip install numpy pip install scikit-learn pip install pydotplus pip installBeautifulSoup

🔧 Technologies Used

• Languages: Python
• IDEs: Visual Studio Code, PyCharm
• Libraries: Pandas, NumPy, Matplotlib, Scikit-Learn

📜 License

This project is licensed under the MIT License - see the LICENSE file for details.

📬 Contact

For any questions, please contact the course instructor or visit the discussion board.

• Email: Mail
• LinkedIn: Farha Kousar
• GitHub: FarhaKousar1601

🌐Looking forward to connecting with you!