Advanced Level Tasks

[Nagasathvik]

1. Image Converter:

   • Description: This program accepts images in various formats such as JPEG, PNG, BMP, and GIF, and converts them into a desired format using the Python Imaging Library (PIL).
   • Implementation: The program uses the Image module from the PIL library to open and save images in different formats. It checks the input image format and converts it to the desired format using the convert() method.
   • Code Explanation: The compress_image() function takes the input image path, desired output path, and format as input parameters. It opens the input image, converts it to the desired format, and saves the converted image to the output path.

2. Data Analysis with Pandas:

   • Description: This task involves loading the "Iris" dataset from Seaborn and performing data analysis using Pandas. It includes exploratory data analysis, cleaning, aggregation, visualizations, and correlation calculations.
   • Implementation: The program uses Pandas to load the Iris dataset and perform various data analysis tasks such as checking for missing values, exploring data distributions, creating summary statistics, visualizing data using matplotlib or seaborn, and calculating correlations between variables.
   • Code Explanation: The code involves loading the Iris dataset using Seaborn's load_dataset() function, converting it to a Pandas DataFrame, and then performing various data analysis tasks using Pandas methods such as describe(), info(), isnull(), groupby(), and visualization using matplotlib or seaborn.

3. Linear Regression with Scikit-learn:

   • Description: This task applies linear regression to predict house prices from the Boston housing dataset using scikit-learn. It compares train and test scores and plots residuals.
   • Implementation: The program uses scikit-learn's LinearRegression class to create a linear regression model, train it on the Boston housing dataset, and evaluate its performance on training and testing data. It also plots residuals to assess the model's performance.
   • Code Explanation: The code involves loading the Boston housing dataset from scikit-learn's datasets module, splitting the data into training and testing sets, creating and training a linear regression model, predicting house prices on the testing set, calculating R-squared scores for training and testing sets, and plotting residuals.

4. Image Compression:

   • Description: This program is a Python tool for compressing images while maintaining quality. It explores compression techniques like RLE (Run-Length Encoding) and DCT (Discrete Cosine Transform), allows users to adjust compression quality, supports various image formats, and provides output options.
   • Implementation: The program uses PIL to open and save images, and implements compression techniques such as RLE and DCT to reduce image size while preserving quality. It provides options for users to adjust compression quality, select input and output image formats, and specify output paths.
   • Code Explanation: The compress_image() function accepts input image path, output image path, and compression quality as input parameters. It opens the input image, applies compression techniques to reduce its size while maintaining quality, and saves the compressed image to the output path.