Demonstrating how changes in input image resolution affect the algorithm's output
-
Updated
Mar 14, 2024
Demonstrating how changes in input image resolution affect the algorithm's output
Embarking on a vital linguistic preservation initiative, our project surveyed the diminishing popularity of rural languages across 20 states of India. Leveraging the power of Computer Science, we meticulously analyzed and visualized the data using Heatmap with Seaborn (sns) in Jupyter Notebook.
This repository contains some of the selected data-science projects.
Seaborn is a visualization library for Python that builds on matplotlib and pandas. It provides beautiful default styles and color palettes for different types of plots, such as histograms, distributions, regression, and matrix plots.
Student Exercise | EDA | Lending Club Case Study
911 Calls Capstone Project - Analysing the frequency of emergency 911 calls. This project is developed on a Jupyter notebook using Python with Numpy, Pandas data analysis libraries along with Matplotlib and seaborn data visualization libraries.
In this project, we aim to predict whether a particular customer will switch to another telecom provider or not, a process referred to as churning and not churning in telecom terminology.
This is my repository where I try to create a Predictive Model in Python for NFL touchdowns.
This project focuses on analyzing Diwali sales data using Python's data manipulation and visualization libraries - pandas, matplotlib, and seaborn.
Analysis of Choco-Bars groceries dataset using python data analysis tool. In this project pandas and numpy libraries are used with matplotlib.pyplot and seaborn visualization library.
This project involves a case study of a real estate company with a dataset containing property prices in the Delhi region. The goal is to optimize the sale prices of properties based on important factors such as area, bedrooms, parking, etc.
To identify the variables affecting house prices, e.g. area, number of rooms, bathrooms, etc.To create a linear model that quantitatively relates house prices with variables such as number of rooms, area, number of bathrooms, etc.To know the accuracy of the model, i.e. how well these variables can predict house prices.
This repository contains Data Engineering projects.
This program provides data loading, exploration, and analysis tools, including descriptive statistics, column categorization, target variable summaries, and correlation analysis. With error handling, it enables seamless exploration and insights extraction from datasets.
Pokemon Exploratory Data Analysis
Data Analytics with Python
To predict delivery time based on sorting time using simple linear regression model.
Data analysis of Instacart's grocery basket and customer profiling with evaluation of their shopping habits. (Career Foundry - Data Analytics project)
Learned techniques and tools for Knowledge Discovery and Data Mining: R, RStudio, Classification Models
Analyzed behavior event data from Southern High School, focusing on race and gender disparities.
Add a description, image, and links to the seaborn-python topic page so that developers can more easily learn about it.
To associate your repository with the seaborn-python topic, visit your repo's landing page and select "manage topics."