Showing results for attend PNG
GitHub Repo
https://github.com/VMThakkar/README.md
VMThakkar/README.md
# PYTHON AND MACHINE LEARNING NATIONAL WORKSHOP Hi I attended 7 Days National Workshop on "Python And Machine Learning" from 07-13 June 2021, conducted by <b> THE CODE SCHOLAR</b>. <br>I got to have hands on experience on: <li>Python <li>Machine Learning <li>Project Understanding <br> During these 7 days, everything was explained from the very basics so that anyone with zero experience on programming can learn. The instructor during the session was Mr. Nitesh. <br>Speakers from different industry domains, lightened up the session with their experience. I got to learn a lot during these 7 days and it was an amazing experience learning with THE CODE SCHOLAR.<br> <br><br>Here's the link for you to watch the sessions as well<br> <a href="https://www.youtube.com/watch?v=feCL8qbjgN0&list=PL3Hnv9OFTJvW4zFKj0qXOpkoNe4AQTzCF&index=1"> <img src="https://github.com/thecodescholar/tcs_data/blob/main/PYTHON%20AND%20MACHINE%20LEARNING.png"> </a> I enjoyed these 7 days, you can as well. To register for next free 7 days bootcamp, visit: <a href="http://www.thecodescholar.com"> www.thecodescholar.com </a> or follow THE CODE SCHOLAR on: <li><a href= "https://linkedin.com/company/the-code-scholar">LinkedIn</a> <li><a href= "https://www.instagram.com/thecodescholar">Instagram</a> <li><a href= "https://youtube.com/channel/UCyG-UNr0u8rIb3Dxq2TAZ9A">YouTube</a> <li><a href= "https://github.com/thecodescholar">GitHub</a> <li><a href= "https://twitter.com/thecodescholar_">Twitter</a>
GitHub Repo
https://github.com/pnguenda/pandas-challenge
pnguenda/pandas-challenge
# Pandas Homework - Pandas, Pandas, Pandas ## Background The data dive continues! Now, it's time to take what you've learned about Python Pandas and apply it to new situations. For this assignment, you'll need to complete **one of two** (not both) Data Challenges. Once again, which challenge you take on is your choice. Just be sure to give it your all -- as the skills you hone will become powerful tools in your data analytics tool belt. ### Before You Begin 1. Create a new repository for this project called `pandas-challenge`. **Do not add this homework to an existing repository**. 2. Clone the new repository to your computer. 3. Inside your local git repository, create a directory for the Pandas Challenge you choose. Use folder names corresponding to the challenges: **HeroesOfPymoli** or **PyCitySchools**. 4. Add your Jupyter notebook to this folder. This will be the main script to run for analysis. 5. Push the above changes to GitHub or GitLab. ## Option 1: Heroes of Pymoli  Congratulations! After a lot of hard work in the data munging mines, you've landed a job as Lead Analyst for an independent gaming company. You've been assigned the task of analyzing the data for their most recent fantasy game Heroes of Pymoli. Like many others in its genre, the game is free-to-play, but players are encouraged to purchase optional items that enhance their playing experience. As a first task, the company would like you to generate a report that breaks down the game's purchasing data into meaningful insights. Your final report should include each of the following: ### Player Count * Total Number of Players ### Purchasing Analysis (Total) * Number of Unique Items * Average Purchase Price * Total Number of Purchases * Total Revenue ### Gender Demographics * Percentage and Count of Male Players * Percentage and Count of Female Players * Percentage and Count of Other / Non-Disclosed ### Purchasing Analysis (Gender) * The below each broken by gender * Purchase Count * Average Purchase Price * Total Purchase Value * Average Purchase Total per Person by Gender ### Age Demographics * The below each broken into bins of 4 years (i.e. <10, 10-14, 15-19, etc.) * Purchase Count * Average Purchase Price * Total Purchase Value * Average Purchase Total per Person by Age Group ### Top Spenders * Identify the the top 5 spenders in the game by total purchase value, then list (in a table): * SN * Purchase Count * Average Purchase Price * Total Purchase Value ### Most Popular Items * Identify the 5 most popular items by purchase count, then list (in a table): * Item ID * Item Name * Purchase Count * Item Price * Total Purchase Value ### Most Profitable Items * Identify the 5 most profitable items by total purchase value, then list (in a table): * Item ID * Item Name * Purchase Count * Item Price * Total Purchase Value As final considerations: * You must use the Pandas Library and the Jupyter Notebook. * You must submit a link to your Jupyter Notebook with the viewable Data Frames. * You must include a written description of three observable trends based on the data. * See [Example Solution](HeroesOfPymoli/HeroesOfPymoli_starter.ipynb) for a reference on expected format. ## Option 2: PyCitySchools  Well done! Having spent years analyzing financial records for big banks, you've finally scratched your idealistic itch and joined the education sector. In your latest role, you've become the Chief Data Scientist for your city's school district. In this capacity, you'll be helping the school board and mayor make strategic decisions regarding future school budgets and priorities. As a first task, you've been asked to analyze the district-wide standardized test results. You'll be given access to every student's math and reading scores, as well as various information on the schools they attend. Your responsibility is to aggregate the data to and showcase obvious trends in school performance. Your final report should include each of the following: ### District Summary * Create a high level snapshot (in table form) of the district's key metrics, including: * Total Schools * Total Students * Total Budget * Average Math Score * Average Reading Score * % Passing Math (The percentage of students that passed math.) * % Passing Reading (The percentage of students that passed reading.) * % Overall Passing (The percentage of students that passed math **and** reading.) ### School Summary * Create an overview table that summarizes key metrics about each school, including: * School Name * School Type * Total Students * Total School Budget * Per Student Budget * Average Math Score * Average Reading Score * % Passing Math (The percentage of students that passed math.) * % Passing Reading (The percentage of students that passed reading.) * % Overall Passing (The percentage of students that passed math **and** reading.) ### Top Performing Schools (By % Overall Passing) * Create a table that highlights the top 5 performing schools based on % Overall Passing. Include: * School Name * School Type * Total Students * Total School Budget * Per Student Budget * Average Math Score * Average Reading Score * % Passing Math (The percentage of students that passed math.) * % Passing Reading (The percentage of students that passed reading.) * % Overall Passing (The percentage of students that passed math **and** reading.) ### Bottom Performing Schools (By % Overall Passing) * Create a table that highlights the bottom 5 performing schools based on % Overall Passing. Include all of the same metrics as above. ### Math Scores by Grade\*\* * Create a table that lists the average Math Score for students of each grade level (9th, 10th, 11th, 12th) at each school. ### Reading Scores by Grade * Create a table that lists the average Reading Score for students of each grade level (9th, 10th, 11th, 12th) at each school. ### Scores by School Spending * Create a table that breaks down school performances based on average Spending Ranges (Per Student). Use 4 reasonable bins to group school spending. Include in the table each of the following: * Average Math Score * Average Reading Score * % Passing Math (The percentage of students that passed math.) * % Passing Reading (The percentage of students that passed reading.) * % Overall Passing (The percentage of students that passed math **and** reading.) ### Scores by School Size * Repeat the above breakdown, but this time group schools based on a reasonable approximation of school size (Small, Medium, Large). ### Scores by School Type * Repeat the above breakdown, but this time group schools based on school type (Charter vs. District). As final considerations: * Use the pandas library and Jupyter Notebook. * You must submit a link to your Jupyter Notebook with the viewable Data Frames. * You must include a written description of at least two observable trends based on the data. * See [Example Solution](PyCitySchools/PyCitySchools_starter.ipynb) for a reference on the expected format. ## Hints and Considerations * These are challenging activities for a number of reasons. For one, these activities will require you to analyze thousands of records. Hacking through the data to look for obvious trends in Excel is just not a feasible option. The size of the data may seem daunting, but pandas will allow you to efficiently parse through it. * Second, these activities will also challenge you by requiring you to learn on your feet. Don't fool yourself into thinking: "I need to study pandas more closely before diving in." Get the basic gist of the library and then _immediately_ get to work. When facing a daunting task, it's easy to think: "I'm just not ready to tackle it yet." But that's the surest way to never succeed. Learning to program requires one to constantly tinker, experiment, and learn on the fly. You are doing exactly the _right_ thing, if you find yourself constantly practicing Google-Fu and diving into documentation. There is just no way (or reason) to try and memorize it all. Online references are available for you to use when you need them. So use them! * Take each of these tasks one at a time. Begin your work, answering the basic questions: "How do I import the data?" "How do I convert the data into a DataFrame?" "How do I build the first table?" Don't get intimidated by the number of asks. Many of them are repetitive in nature with just a few tweaks. Be persistent and creative! * Expect these exercises to take time! Don't get discouraged if you find yourself spending hours initially with little progress. Force yourself to deal with the discomfort of not knowing and forge ahead. Consider these hours an investment in your future! * As always, feel encouraged to work in groups and get help from your TAs and Instructor. Just remember, true success comes from mastery and _not_ a completed homework assignment. So challenge yourself to truly succeed! ### Copyright Trilogy Education Services © 2019. All Rights Reserved.
GitHub Repo
https://github.com/Mariovontee123/Prince-Vontee-
Mariovontee123/Prince-Vontee-
<!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <meta http-equiv="X-UA-Compatible" content="IE=edge"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>Prince Vontee</title> </head> <body> <h1>Prince VonteeS</h1> <nav> <a href="three.html">One</a> <a href="one.html">Two</a> <a href="wilkin.html">Three</a> </nav> <h2>Materials for Purchase</h2> <ul> <li>Computer</li> <li>Disk Drive</li> <li>Cable</li> <li>Charger</li> <li>Extension Coil</li> <li>Screen</li> </ul> <h2>Achievements</h2> <label for="file">Progress in this course (100%)</label> <progress id="file" value="100" max="100"></progress><br> <label for="file">Progress in the Specialization of Networking (20%)</label> <progress id="file" value="20" max="100"></progress><br> <label for="file">Progress in Web Design goals (50%)</label> <progress id="file" value="50" max="100"></progress> <h2>More About Me</h2> <head>My childhood</head> <p>Am prince Vontee, i attended the ST. matthew united methodist senior hihg school in logan town.<br> </p> <img src="http://www.intro-webdesign.com/images/newlogo.png" alt=""> <h3>This Webpage was developed by prince vontee & colleen van lent. To learn more about web design, visit <a href="http://www.intro-webdesign.com">Intro to Web Design.</a></h3> </body> </html>
GitHub Repo
https://github.com/SHIVAMBASIA/Bosch-Hackathon-Solution
SHIVAMBASIA/Bosch-Hackathon-Solution
The following repository is my solution for the Bosch hackathon.I was even called for the final phase of the hackathon in the Robert Bosch's Bangalore office though I could not attend the final hackathon phase due to been out of India that point of time.A text file data.txt was given containing the commentary of one innings of a cricket match where I have to generate the score card as given in Scorecard_Format.png.The output file I generated is scorecard.txt and the screenshot of the scorecard generated is the Scorecard.png file.
GitHub Repo
https://github.com/MAHESH7295/MAHESH-