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base repository: CSEdgeOfficial/Python-Programming-Internship
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head repository: dora-b72/Python-Programming-Internship
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- 6 commits
- 25 files changed
- 2 contributors
Commits on Jun 10, 2024
Commits on Jun 12, 2024
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### Simple Calculator To solve the Simple Calculator task, we can use Python’s basic arithmetic operators for performing addition, subtraction, multiplication, and division. We'll capture user inputs and apply conditional statements to execute the desired operations, providing error handling for cases like division by zero. ### To-Do List For the To-Do List task, we can develop a list to store tasks and create functions to add, delete, and mark tasks as completed. Using either a console-based interface or a GUI with Tkinter, we can interact with the user to manage their tasks effectively. ### Number Guessing Game In the Number Guessing Game task, we can generate a random number using Python’s `random` module. We then use a loop to prompt the player for guesses, compare each guess to the target number, and provide feedback until the player guesses correctly or runs out of attempts. ### PDF Converter To build the PDF Converter, we can use libraries like PyPDF2 for extracting text and pdf2image for converting PDF pages to images. We will implement functions to handle file input/output and allow the user to choose the desired output format for conversion. ### Weather App For the Weather App, we can fetch weather data from an API like OpenWeatherMap using the `requests` library. By parsing the JSON response, we can extract and display current weather conditions, forecasts, and temperature trends. ### Web Scraper In the Web Scraper task, we can use libraries such as BeautifulSoup and requests to fetch and parse HTML content from websites. Extracted data can then be stored in structured formats like CSV or JSON using Python’s built-in modules. ### Chatbot To build a simple chatbot, we can use natural language processing libraries like NLTK or spaCy. By defining responses based on pattern matching or predefined rules, we can create a program that responds to user queries and provides relevant information. ### PDF Merger/Splitter For the PDF Merger/Splitter, we can employ PyPDF2 to read and manipulate PDF files. The program can merge multiple PDFs into a single file or split a PDF into multiple smaller files based on specified page ranges. ### Image Converter To solve the Image Converter task, we can use the Python Imaging Library (PIL) to accept images in formats like JPEG, PNG, BMP, and GIF and convert them into a desired format. The program will handle various image processing tasks efficiently. ### Data Analysis with Pandas For the Data Analysis with Pandas task, we can load the "Iris" dataset from Seaborn and perform exploratory data analysis using Pandas. This includes cleaning, aggregating, visualizing data, and calculating correlations to derive meaningful insights. ### Linear Regression with Scikit-learn To apply linear regression for predicting house prices, we can use the Boston housing dataset with scikit-learn. We will split the data into training and testing sets, fit a linear model, and compare performance metrics while visualizing residuals. ### Image Compression In the Image Compression task, we can develop a tool using Python to compress images while maintaining quality. By exploring compression techniques like RLE and DCT, we can allow users to adjust compression settings and support various image formats.
Commits on Jun 16, 2024
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