Great Deal! Get Instant $10 FREE in Account on First Order + 10% Cashback on Every Order Order Now

You are to submit either a GitHub (for a statistic visualization) or a shinyapps.io (for an interactive visualization) link to your final project (remember to include the data in your repo so I can...

1 answer below »
You are to submit either a GitHub (for a statistic visualization) or a shinyapps.io (for an interactive visualization) link to your final project (remember to include the data in your repo so I can run your code). In addition, you are to submit a link to the GitHub repository with your code (two links total).

The total number of points assigned to your final project submission is 20, distributed as follows:

2 points for a working link to GitHub (for a statistic visualization) or a shinyapps.io (for an interactive visualization)
2 points for a description of the data and where the data was acquired
6 points, divided into 2 points for each of three plots (for a total of three different types of plot, from the different types we've seen in class)
2 points for the use of colorblind-friendly color schemes
2 points for the use of the appropriate color scheme (categorical, divergent, or continuous) given the variable mapped to the color/fill aesthetics
2 points for appropriate axes scales and labels (meaning they are legible, not overlapping, and clearly state what is being displayed in the plot)
2 points for titles and captions that make it clear what the plot is about
2 points for appropriate ordering of group levels (examples: unordered categorical variables are displayed not according to alphabetical order, but reordered by the numeric variable used; ordered categorical variables are shown in their correct order)
Again, what you need to submit is a LINK to your visualization and a LINK to your GitHub repo with your code.
Answered Same Day Oct 15, 2021

Solution

Nithin answered on Oct 15 2021
128 Votes
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "DataVizualisation.Ipynb",
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "qS8EH-GntbnQ"
},
"source": [
"## Data Visualuization Assignment\n",
"\n",
"#### Name : \n",
"\n",
"#### Link : https:
colab.research.google.com/drive/1IzkpnxS6bZY7hKeNQ-x0dHK6
Sow0OQ?usp=sharing"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "FwOXPYrHtRj7"
},
"source": [
"## Importing necessary modules"
]
},
{
"cell_type": "code",
"metadata": {
"id": "-qjpjHQKVn_7"
},
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns"
],
"execution_count": 1,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "SwtEp_FTYkTd"
},
"source": [
"## Loading Dataset"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https:
localhost:8080/",
"height": 424
},
"id": "Dl3QUnheYDw3",
"outputId": "db0e2e6c-86b9-4a51-c220-61954cd06f
"
},
"source": [
"dataset = pd.read_csv('Iris.csv')\n",
"dataset"
],
"execution_count": 3,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"
\n",
"