Part 1: Foundational Python Programs (Basic Level)

This section covers the core concepts of the Python language from Unit III, providing the essential building blocks for all subsequent programming tasks.

1. Program to Demonstrate Data Types, Operators, and I/O This program covers the fundamentals of variables, data types, arithmetic operators, and basic input/output operations.

2. Program for Control Structures (Conditional, Looping, Exceptions) This program demonstrates how to control the flow of a program using if-elif-else statements, for and while loops, and try-except blocks for handling errors.   

3. Program for Data Structures (Lists, Tuples, Sets, Dictionaries) This program illustrates the creation and basic manipulation of Python's core data structures, highlighting the difference between mutable and immutable types.   

4. Program for User-Defined Functions and Scope This program shows how to define and call functions, pass different types of arguments, and demonstrates the difference between local and global variable scope.   

5. Program for File Handling This program demonstrates essential file operations: writing to a file, appending content, and reading from it. The with statement ensures the file is closed automatically.   

6. Program for Object-Oriented Programming (OOP) This program introduces OOP concepts by creating a Car class with attributes and methods, and then an ElectricCar class that inherits from it.   

Part 2: Data Science Workflow Programs (Intermediate Level)

This section covers the practical workflow of a data scientist from Unit IV and introduces the core libraries from Unit I.

7. Program for Data Ingestion with Pandas This program demonstrates reading data from CSV and Excel files into a pandas DataFrame, a primary data structure for analysis.

8. Program for Data Exploration and Wrangling This program shows how to inspect a DataFrame and perform basic data cleaning, such as handling missing values and binning data.   

9. Program for Data Visualization with Matplotlib This program demonstrates how to create common chart types to visualize data from a DataFrame.   

Part 3: Machine Learning Implementation Programs (Advanced Level)

This section covers the implementation of various machine learning algorithms from Unit II using the scikit-learn library.

10. Program for Linear and Non-Linear (Polynomial) Regression This program builds a simple linear regression model and a polynomial regression model to predict a continuous value.   

11. Program for Classification Algorithms This program demonstrates several key classification algorithms on a sample dataset, including KNN, Decision Tree, and SVM.   

12. Program for Clustering Algorithms This program applies unsupervised learning algorithms like K-Means and DBSCAN to group unlabeled data.   

13. Program for a Simple Content-Based Recommender System This program builds a basic recommender system that suggests items based on text feature similarity (e.g., movie descriptions).   

Part 4: Programs for Specialized Libraries (Advanced Level)

This section provides code examples for the specialized libraries mentioned in Unit I, such as Seaborn for advanced visualization, Pillow and Mahotas for image processing, and NLTK/FlashText for natural language processing.

14. Program for Advanced Statistical Visualization with Seaborn Seaborn is a library built on top of Matplotlib that provides a high-level interface for drawing attractive and informative statistical graphics. This program demonstrates a pair plot, which shows pairwise relationships between variables in a dataset.  

15. Program for Interactive Visualization with Bokeh Bokeh is a Python library for creating interactive visualizations for modern web browsers. This program creates a simple interactive scatter plot with tools for panning, zooming, and inspecting data points.

16. Program for Image Processing with Pillow Pillow (a fork of PIL) is the go-to library for basic image manipulation. This program demonstrates opening an image, rotating it, converting it to grayscale, applying a filter, and saving the result.   

17. Program for Image Feature Extraction with Mahotas Mahotas is a library for computer vision and image processing that operates on NumPy arrays. It's known for its speed. This program calculates Haralick texture features, which are useful for image classification tasks.

18. Program for Natural Language Processing with NLTK The Natural Language Toolkit (NLTK) is a foundational library for NLP. This program demonstrates a basic text preprocessing pipeline: tokenization, stop word removal, and lemmatization.

19. Program for Fast Keyword Matching with FlashText FlashText is an optimized library for searching and replacing keywords in text, often much faster than regular expressions for a large number of keywords.

Part 5: More Machine Learning Programs (Advanced Level)

This section provides implementations for additional algorithms and concepts from Unit II, including Hierarchical Clustering and the Naive Bayes classifier, as well as a more detailed look at model evaluation.

20. Program for Hierarchical Clustering with SciPy This program uses SciPy to perform agglomerative hierarchical clustering and Matplotlib to visualize the result as a dendrogram, which is a key advantage of this clustering method.

21. Program for Naive Bayes Classification This program implements the Gaussian Naive Bayes algorithm, which is suitable for continuous data that follows a Gaussian (normal) distribution.

22. Program for Advanced Model Evaluation Accuracy isn't always the best metric, especially for imbalanced datasets. This program demonstrates how to generate a more detailed classification_report and a confusion_matrix to better understand a model's performance.