Data visualization - qualitative & quantitative data (Airbnb listings for NY)

PyVizNotebook is a collection of Matplotlib visualizations demonstrating a wide range of plot types and techniques for data visualization. Whether you're a beginner looking to learn or an experienced developer seeking inspiration, this repository offers a diverse set of examples to explore.

Popularity of engine fuel types (1982 - 2016)

This is a practice Repository consisting of all the notebooks I have practiced to learn python for data science from basics to Advance.

This repository consists of notebooks where I learned the basics of getting started with data analysis using Python libraries

Welcome to the Data preprocessing Repository! This repository is dedicated to showcase the comprehensive resources and implementations related to Data Preprocessing using Python and Jupyter Notebook.

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.

Data analysis project on Diwali sales using Python libraries like Pandas, Matplotlib, and Seaborn. The notebook includes gender, age group, state-wise, and marital status-based purchasing insights to help understand customer behavior.

Career Foundry data analytics project to provide a client recommendations on a marketing strategy. Jupyter notebooks include cleaning and merging data along with creating new columns to offer the best understanding of consumers' interactions with products.

The uploaded file is a Jupyter Notebook titled "Flight Analysis". It likely involves analyzing flight-related data, potentially exploring trends, patterns, or insights using data science techniques. The analysis might include data visualization, statistical analysis, or predictive modeling.

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.

Using Python libraries in a Jupyter Notebook, this project explores Diwali sales data, revealing valuable insights: Buyer Dynamics: Females drive sales, showcasing higher purchasing power than males. Age Impact: The 26-35 age group, primarily females, contributes significantly.Marital Influence: Married women exhibit strong purchasing potential.