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These problems relate to Jupyter, numpy, and pyplot. We will use the famous iris data set. Save your work as a single Jupyter notebook file in a GitHub repository. Include any required data files, a README, and a gitignore file in the repository.
A study to compare the performance of the company's drug of interest, Capomulin, versus the other treatment regimens. Generate all of the tables and figures needed for the technical report of the study and a top-level summary of the study results.
This is for reference purpose. Plotly helps to create interactive charts. This package mainly helps for data scientists for creative productive graphs, Mainly in presentation time it is useful
Interactive data visualization using D3.js to show some state-level data about three risk factors (obesity, smoking and lack of healthcare) plotted against three perhaps underlying factors (income, poverty rate and age) based on 2014 U.S. Census data. Find it here: https://sheetalbongale.github.io/D3-Data-Journalism/
This project is in two parts. First, WeatherPy visualizes the weather of 500+ cities across the world of varying distances from the equator, using Python script, CityPy, and OpenWeatherMap APIs. The second part, VacationPy, uses Jupiter-Gmaps and the Google Places API to create a heatmap and filter down cities.
HBFC Bank's project analyzes 5000 customer records, aiming to convert depositors into personal loan customers. Advanced skills in statistical analysis and tools like Excel uncover key insights, including a 9.6% personal loan uptake. Diverse demographics and strategic findings inform recommendations for future marketing campaigns.
A Python tool for analyzing and visualizing Iris flower measurements. Includes bar charts and scatter plots. Great for data analysis and visualization.