Python Project Topics for Computer Science

Python Project Topics for Computer Science are suggested by us if you are looking for experts touch then we will give you tailored guidance. Drop a message to matlabsimulation.com all your research requirements we will give you instant update. In order to resolve a problem, one or more algorithms can be employed in every project. For final year projects or coursework, these topics are more ideal:

1. Pathfinding Algorithms in a Maze

• Outline: A Python application has to be created which solves a maze by applying different pathfinding algorithms. Algorithms such as Breadth-First Search (BFS), Dijkstra’s, and A* should be encompassed by the project.
• Algorithm Focus: BFS, Dijkstra’s algorithm, and A* algorithm.
• Major Characteristics: Comparison of algorithm functionality (space and time difficulty), and visualization of the maze and the pathfinding operation.

2. Genetic Algorithm for Solving the Traveling Salesman Problem (TSP)

• Outline: To discover an approximate approach to the Traveling Salesman Problem, a genetic algorithm has to be applied in Python. In this problem, discovering the shortest potential path is the major goal which explores every city one time and returns to the origin city.
• Algorithm Focus: Genetic Algorithm (GA).
• Major Characteristics: Functionality comparison with brute force approaches, visualization of paths, selection, mutation, crossover, and population generation.

3. Implementation of a Cryptographic Algorithm

• Outline: A Python program has to be developed, which carries out message encryption and decryption by applying a cryptographic algorithm like DES, AES, or RSA. Key generation and handling must be encompassed in the project.
• Algorithm Focus: DES, AES, or RSA encryption algorithms.
• Major Characteristics: Safety analysis, private/public key handling, key generation, and encryption and decryption of messages.

4. Data Compression using Huffman Coding

• Outline: A Python tool has to be created, which utilizes Huffman coding to compress and decompress files. For lossless data compression, it is considered as an optimal prefix code.
• Algorithm Focus: Huffman Coding algorithm.
• Major Characteristics: Comparison of compression ratios, visualization of the Huffman tree, decoding back to original data, and encoding of data to compressed form.

5. Implementing and Analyzing Sorting Algorithms

• Outline: To apply different sorting algorithms (for instance: Bubble Sort, HeapSort, MergeSort, and QuickSort), we plan to develop a Python application. In order to input data and visualize the sorting operation, the users have to be enabled by the program.
• Algorithm Focus: Bubble Sort, HeapSort, MergeSort, and QuickSort.
• Major Characteristics: Functionality metrics for various data types and sizes, comparison of sorting algorithms, time complexity analysis, and sorting visualizer.

6. AI-Based Game Player using Minimax Algorithm

• Outline: For a two-player game (for instance: Chess, Connect Four, or Tic-Tac-Toe), an AI player has to be applied. To improve the decision-making operation, the Minimax algorithm must be utilized with alpha-beta pruning.
• Algorithm Focus: Minimax algorithm with alpha-beta pruning.
• Major Characteristics: Visualization of the decision tree, assessment of game conditions, AI decision-making operation, and game interface.

7. Building a Recommendation System using Collaborative Filtering

• Outline: A suggestion system has to be created, which considers collaborative filtering algorithms such as item-related or user-related collaborative filtering to recommend movies, products, or content to users.
• Algorithm Focus: Collaborative Filtering (item-related and user-related).
• Major Characteristics: Assessment of suggestion quality, suggestion generation, similarity calculation (for instance: Pearson correlation, cosine similarity), and user-item matrix creation.

8. Implementation of a Graph-Based Search Engine

• Outline: A Python-related search engine must be developed, which employs graph algorithms to crawl web pages and rank them on the basis of significance. For ranking, we aim to apply PageRank or other graph-related algorithms.

• Algorithm Focus: BFS/DFS, and PageRank algorithm for web crawling.
• Major Characteristics: Functionality analysis, search query interface, ranking of pages, graph creation, and web crawling.

9. Machine Learning Model Optimization using Genetic Algorithms

• Outline: For a machine learning design (for instance: SVM, a neural network), the hyperparameters have to be enhanced by applying a genetic algorithm. In what way the model functionality is optimized by genetic algorithms should be shown by the project.
• Algorithm Focus: Genetic Algorithm for hyperparameter enhancement.
• Major Characteristics: Functionality comparison with grid search/random search, hyperparameter tuning utilizing GA, and machine learning design execution.

10. Building a Text Summarizer using TextRank Algorithm

• Outline: A Python application has to be created, which utilizes TextRank algorithm to outline long text documents. For text processing, it is considered as an unsupervised graph-related ranking design.
• Algorithm Focus: TextRank algorithm (related on PageRank).
• Major Characteristics: Assessment against human summaries, ranking sentences to produce summaries, graph creation for sentences/phrases, and text processing and cleaning.

11. Implementation of the K-Means Clustering Algorithm

• Outline: To cluster data points into k groups, the K-Means clustering algorithm must be employed. For that, a Python application should be developed. An analysis of the convergence of algorithm and visualization of the clusters must be encompassed in the project.
• Algorithm Focus: K-means clustering algorithm.
• Major Characteristics: Functionality metrics (for instance: silhouette score, inertia), cluster visualization, repetitive refinement, cluster initialization, and data input and preprocessing.

12. Dynamic Programming Solution for the Knapsack Problem

• Outline: By means of dynamic programming, we intend to apply an approach to the 0/1 Knapsack issue. With a brute-force approach, a comparison has to be involved by the project.
• Algorithm Focus: Dynamic Programming (DP) for Knapsack issue.
• Major Characteristics: Space and time complexity analysis, visualization of decision matrix, comparison with brute-force approach, and implementation of DP solution.

13. Implementing a Priority Queue using Binary Heap

• Outline: With the support of a binary heap, a priority queue data structure must be created in Python. Operations such as extraction of the least element, deletion, and insertion have to be facilitated in this project.

• Algorithm Focus: Binary Heap for implementing Priority Queue.
• Major Characteristics: Functionality analysis, visualization of the heap, and heap processes (insert, delete, extract-min).

14. Building a Spell Checker using Levenshtein Distance

• Outline: A Python-related spell checker should be developed, which utilizes the Levenshtein distance (edit distance) algorithm to recommend corrections for incorrect terms.
• Algorithm Focus: Levenshtein Distance (edit distance) algorithm.
• Major Characteristics: Functionality and accuracy assessment, dictionary execution, recommendation generation, and spell checking.

15. Implementation of the Apriori Algorithm for Market Basket Analysis

• Outline: A Python program has to be created, which identifies regular itemsets in transaction data by employing an Apriori algorithm. For market basket analysis, it should also generate association rules.
• Algorithm Focus: Apriori algorithm.
• Major Characteristics: Visualization of association procedures, rule generation with assist and confidence, frequent itemset generation, and data preprocessing.

16. Graph Coloring Problem using Backtracking

• Outline: For the graph coloring problem, a Python approach has to be applied. In this problem, coloring the nodes of a graph is the major objective, in which the similar color should not be presented in the two nearby nodes. To discover an approach, backtracking must be employed.
• Algorithm Focus: Backtracking for Graph Coloring.
• Major Characteristics: Comparison with greedy algorithms, visualization of colored graphs, backtracking execution, and graph representation.

17. Building a File Compression Tool using LZ77 Algorithm

• Outline: To utilize the LZ77 compression algorithm, a Python-related file compression tool must be created. This algorithm is referred to as a dictionary-related compression scheme. The files have to be compressed and decompressed by the tool.
• Algorithm Focus: LZ77 compression algorithm.
• Major Characteristics: Functionality metrics, comparison with other compression algorithms, file reading and processing, compression, and decompression.

18. Implementation of a Decision Tree Classifier

• Outline: For a decision tree classifier, a Python application has to be developed from scratch. In terms of information gain or Gini impurity, the tree should be constructed in this project. Then, the decision tree must be visualized.
• Algorithm Focus: Decision Tree algorithm (ID3, CART).

• Major Characteristics: Accuracy assessment, visualization of the decision tree, classification of novel examples, pruning, and tree building.

19. Solving Sudoku using Backtracking

• Outline: A Python program should be created, which employs the backtracking algorithm to resolve Sudoku puzzles. As input, an unsolved Sudoku board must be taken by the program. Then, a solved board has to be returned.
• Algorithm Focus: Backtracking algorithm for solving Sudoku.
• Major Characteristics: Management of diverse complexity levels, visualization of the solving process, backtracking execution, and sudoku board representation.

20. Implementation of the Bellman-Ford Algorithm for Shortest Path

• Outline: In a graph, the shortest route must be discovered from a single source to all other nodes. For that, the Bellman-Ford algorithm has to be applied in Python. With negative weights, the graphs have to be managed.
• Algorithm Focus: Bellman-Ford algorithm.
• Major Characteristics: Functionality comparison with Dijkstra’s algorithm, detection of negative weight cycles, shortest path calculation, and graph representation.

On the basis of Python usage, we recommended numerous fascinating projects, which are suitable for final year projects or coursework. For each project, a concise outline, algorithm focus, and major characteristics are provided by us.