1st place solution for the Kaggle PANDA Challenge

Comprehensive repository featuring solutions to diverse programming challenges in Python, MySQL, JavaScript, and Pandas, catering to both beginners and experienced developers.

Code related to the PANDA challenge on artificial intelligence for Gleason grading

This is a repository which is a collection of solutions to the 30 Day Challenge involving database questions being solved through Pandas, yes they can be solved using SQL as well but this contains pandas only . Hope you like it :)

This project implements a solution for bypassing CAPTCHAs in web scraping using Python with Selenium and Pandas, along with a third-party API. The application automates the process of navigating web pages and extracting data while effectively handling CAPTCHA challenges, enabling seamless data collection from websites that employ CAPTCHA protection

Pandas Code Challenges

# 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.

probability / statistics /maths / pandas / sql / plots(data analysis) / Machine learning / python code challenge

game challenges for python pandas

Welcome to the new era. One of the biggest challenges when studying the technical skills of data science is understanding how those skills and concepts translate into real jobs, like growth marketing. The main idea is to demonstrate how with Python skills you can make the best marketing decisions based on data. In this project, through Python, using packages such as pandas, I perform an analysis of marketing campaigns using machine learning, taking into account the different metrics such as CTR, conversion rate, or retention rate of each social network, to learn how to analyze campaign performance, measure customer engagement, and predict customer churn, to improve company's marketing strategy.

# Starbucks Promotions Project ### This project is the Capstone Project of Udacity's Machine Learning Engineering Nanodegree program.    ## Problem Statement This data set contains simulated data that mimics customer behavior on the Starbucks rewards mobile app. Once every few days, Starbucks sends out an offer to users of the mobile app. An offer can be merely an advertisement for a drink or an actual offer such as a discount or BOGO (buy one get one free). Some users might not receive any offer during certain weeks. Not all users receive the same offer, and that is the challenge to solve with this data set. The task is to combine transaction, demographic and offer data to determine which demographic groups respond best to which offer type. This data set is a simplified version of the real Starbucks app because the underlying simulator only has one product whereas Starbucks actually sells dozens of products. Starbucks collects the customer data to understand their behaviour on the rewards and offers sent via the mobile-app. Once every few days, Starbucks sends the personalised offers to its customers. These customers can respond positively/negatively/neutrally. A key thing to note is that not all the customers receive the same offer. The task of this project is to combine transaction, demographic and offer data of the past (which is already provided) to determine which demographic groups respond best to which offer types. In order to develop this project, we needed to use some tools, packages, systems and services that could help us achieve our goals. #### Libraries First of all, we used **Python** to write our scripts not only for algorithm training and serving but also for the orchestration of the whole process. Important packages within this environment are listed below: This project is developed in Python 3.6. You will need install some libraries in order to run the code. Libraries are: * `pandas` so we could work with tabular data in dataframes; * `Ploty` so we could visualize our Dataset; * `matplotlib` for Dataset visualization; * `numpy` so we could easily manipulate arrays and data structures; * `seaborn` and `matplotlib` so we could generate insightful visualizations; * `sklearn` so we could build and develop our model pipeline; * `imblearn` so we could apply SMOTE to our training data; * `xgboost` so we could have our main classifier; * `sagemaker` so we could easily interact with AWS. * `json` for reading our Dataset Files. * `boto3` Finally, we used AWS environment in order to launch training jobs, deploy our model and serve predictions. The main services used are also listed below: * __AWS SageMaker__: training, hyperparameter tuning and endpoint serving; * __Amazon S3__: saving our data and model artifacts; ## Files Descriptions This project is structured as follows: #### 01. Proposal Project proposal documentation. #### 02. Data_Cleaning_[Dataset] Folder to perform data preparation and Dataset Cleaning and Prepare the Final Data for Further using in model algorithms. #### 03. Pre-processing Dataset Visualization Folder to perform final Pre-processing Dataset to be used in Visualization and exploration. #### 04. Dataset_Visualization Folder to perform Visualizations for the Pre-processed Dataset. #### 06. ORG_Starbucks_Capstone_Project.ipynb Jupyter notebook file that deploy final model and create an endpoint and orchestrates the end-to-end process in AWS SageMaker and also interacts with other services.

I'm attempting the NYC Taxi Duration prediction Kaggle challenge. I'll by using a combination of Pandas, Matplotlib, and XGBoost as python libraries to help me understand and analyze the taxi dataset that Kaggle provides. The goal will be to build a predictive model for taxi duration time. I'll also be using Google Colab as my jupyter notebook. i will also predict without Google colab on normal system.

O Panda Challenge Downloader é um aplicativo React Native desenvolvido usando o Expo (bare workflow). Ele consome a API do Panda Video para exibir uma lista de vídeos na tela inicial. Ao clicar em um vídeo, os usuários são direcionados para a tela de detalhes do vídeo, onde podem assistir ao vídeo e salvar localmente no dispositivo.

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Simplified solutions to all the problems from the "30 Days of Pandas" Leetcode challenge with explanation.

Build real Pandas projects with 3 beginner-friendly challenges. Learn by doing with guided coding exercises and practical applications.

This repo contains solutions to LeetCode’s 30-Day pandas challenge, showcasing efficient data manipulation techniques. Each day’s problem is solved using pandas functions like groupby, merge, and pivot. Code is well-structured, documented, and ideal for learning pandas through real-world problems. 🚀

# Web Design Homework - Web Visualization Dashboard (Latitude) ## Background Data is more powerful when we share it with others! Let's take what we've learned about HTML and CSS to create a dashboard showing off the analysis we've done.  ### Before You Begin 1. Create a new repository for this project called `Web-Design-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 web challenge. Use a folder name to correspond to the challenge: **WebVisualizations**. 4. Add your **html** files to this folder as well as your **assets**, **Resources** and **visualizations** folders. 5. Push the above changes to GitHub or GitLab. 6. Deploy to GitHub pages. ## Latitude - Latitude Analysis Dashboard with Attitude For this homework we'll be creating a visualization dashboard website using visualizations we've created in a past assignment. Specifically, we'll be plotting [weather data](Resources/cities.csv). In building this dashboard, we'll create individual pages for each plot and a means by which we can navigate between them. These pages will contain the visualizations and their corresponding explanations. We'll also have a landing page, a page where we can see a comparison of all of the plots, and another page where we can view the data used to build them. ### Website Requirements For reference, see the ["Screenshots" section](#screenshots) below. The website must consist of 7 pages total, including: * A [landing page](#landing-page) containing: * An explanation of the project. * Links to each visualizations page. There should be a sidebar containing preview images of each plot, and clicking an image should take the user to that visualization. * Four [visualization pages](#visualization-pages), each with: * A descriptive title and heading tag. * The plot/visualization itself for the selected comparison. * A paragraph describing the plot and its significance. * A ["Comparisons" page](#comparisons-page) that: * Contains all of the visualizations on the same page so we can easily visually compare them. * Uses a Bootstrap grid for the visualizations. * The grid must be two visualizations across on screens medium and larger, and 1 across on extra-small and small screens. * A ["Data" page](#data-page) that: * Displays a responsive table containing the data used in the visualizations. * The table must be a bootstrap table component. [Hint](https://getbootstrap.com/docs/4.3/content/tables/#responsive-tables) * The data must come from exporting the `.csv` file as HTML, or converting it to HTML. Try using a tool you already know, pandas. Pandas has a nifty method approprately called `to_html` that allows you to generate a HTML table from a pandas dataframe. See the documentation [here](https://pandas.pydata.org/pandas-docs/version/0.17.0/generated/pandas.DataFrame.to_html.html) The website must, at the top of every page, have a navigation menu that: * Has the name of the site on the left of the nav which allows users to return to the landing page from any page. * Contains a dropdown menu on the right of the navbar named "Plots" that provides a link to each individual visualization page. * Provides two more text links on the right: "Comparisons," which links to the comparisons page, and "Data," which links to the data page. * Is responsive (using media queries). The nav must have similar behavior as the screenshots ["Navigation Menu" section](#navigation-menu) (notice the background color change). Finally, the website must be deployed to GitHub pages. When finished, submit to BootcampSpot the links to 1) the deployed app and 2) the GitHub repository. Ensure your repository has regular commits (i.e. 20+ commits) and a thorough README.md file ### Considerations * You may use the [weather data](Resources/cities.csv) or choose another dataset. Alternatively, you may use the included [cities dataset](Resources/cities.csv) and pull the images from the [assets folder](Resources/assets). * You must use Bootstrap. This includes using the Bootstrap `navbar` component for the header on every page, the bootstrap table component for the data page, and the Bootstrap grid for responsiveness on the comparison page. * You must deploy your website to GitHub pages, with the website working on a live, publicly accessible URL as a result. * Be sure to use a CSS media query for the navigation menu. * Be sure your website works at all window widths/sizes. * Feel free to take some liberty in the visual aspects, but keep the core functionality the same.

Using pandas to analyze school and student performance within a district

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This project uses only 'matplotlib', 'numpy' and 'pandas' to make candle stick chart from scratch. There are many packages one could use to make a Candlestick Chart very quickly, however, the fun was in the challenge of making it for myself just using. Features a link to a yahoo finance csv file, ability to chose how many days prior to the most recent day to view. This is a similar capability found in popular trading platforms like TD's "Think or Swim". From humble beginnings, we can build more sophisticated functionality.

Week1 Report Here is a quick summary of what I have achieved to learn in my first week of training under ParrotAi. Introduction to Machine learning , I have achieved to know a good intro into Machine Learning which include the history of ML ,the types of ML such supervised, unsupervised, Reinforcement learning. And also answers to questions such why machine learning? , challenges facing machine learning which include insufficient data, irrelevant on data, overfitting, underfitting and there solutions in general. Supervised Machine algorithms, here I learnt the theory and intuition behind the common used supervised ML including the KNN, Linear Regressions, Logistic, Regression, and Ensemble algorithm the Random forest. Also not only the intuition but their implementation in python using the sklearn library and parameter tuning them to achieve a best model with stunning accuracy(here meaning the way to regularize the model to avoid overfitting and underfitting).And also the intuition on where to use/apply the algorithms basing on the problem I.e classification or regression. Also which model performs better on what and poor on what based on circumstances. Data preprocessing and representation here I learnt on the importance of preprocessing the data, also the techniques involved such scaling(include Standard Scaling, RobustScaling and MinMaxScaler) ,handling the missing data either by ignoring(technical termed as dropping) the data which is not recommended since one could loose important patterns on the data and by fitting the mean or median of the data points on the missing places. On data representation involved on how we can represent categorical features so as they can be used in the algorithm, the method learnt here is One-Hot Encoding technique and its implementation in python using both Pandas and Sklearn Libraries. Model evaluation and improvement. In this section I grasped the concept of how you can evaluate your model if its performing good or bad and the ways you could improve it. As the train_test_split technique seems to be imbalance hence the cross-validation technique which included the K-fold , Stratified K-fold and other strategies such LeaveOneOut which will help on the improvement of your model by splitting data in a convenience manner to help in training of model, thus making it generalize well on unseen data. I learnt also on the GridSearch technique which included the best method in which one can choose the best parameters for the model to improve the performance such as the simple grid search and the GridSearch with cross-validation technique, all this I was able to implement them in code using the Sklearn library in python. Lastly the week challenge task given to us was tremendous since I got to apply what I learned in theory to solve a real problem.It was good to apply the workflow of a machine learning task starting from understanding the problem, getting to know the data, data preprocessing , visualising the data to get more insights, model selection, training the model to applying the model to make prediction In general I was able to grasp and learn much in this week from basic foundation of Machine Learning to the implementations of the algorithms in code. The great achievement so far is the intuition behind the algorithm especially supervised ones. Though yet is much to be covered but the accomplishment I have attained so far its a good start to say to this journey on Machine learning. My expectation on the coming week is on having a solid foundation on deep learning.

Part I - WeatherPy In this example, you’ll be creating a Python script to visualize the weather of 500+ cities across the world of varying distance from the equator. To accomplish this, you’ll be utilizing a simple Python library, the OpenWeatherMap API, and a little common sense to create a representative model of weather across world cities. Your first objective is to build a series of scatter plots to showcase the following relationships: Temperature (F) vs. Latitude Humidity (%) vs. Latitude Cloudiness (%) vs. Latitude Wind Speed (mph) vs. Latitude After each plot add a sentence or too explaining what the code is and analyzing. Your next objective is to run linear regression on each relationship, only this time separating them into Northern Hemisphere (greater than or equal to 0 degrees latitude) and Southern Hemisphere (less than 0 degrees latitude): Northern Hemisphere - Temperature (F) vs. Latitude Southern Hemisphere - Temperature (F) vs. Latitude Northern Hemisphere - Humidity (%) vs. Latitude Southern Hemisphere - Humidity (%) vs. Latitude Northern Hemisphere - Cloudiness (%) vs. Latitude Southern Hemisphere - Cloudiness (%) vs. Latitude Northern Hemisphere - Wind Speed (mph) vs. Latitude Southern Hemisphere - Wind Speed (mph) vs. Latitude After each pair of plots explain what the linear regression is modelling such as any relationships you notice and any other analysis you may have. Your final notebook must: Randomly select at least 500 unique (non-repeat) cities based on latitude and longitude. Perform a weather check on each of the cities using a series of successive API calls. Include a print log of each city as it’s being processed with the city number and city name. Save a CSV of all retrieved data and a PNG image for each scatter plot. Part II - VacationPy Now let’s use your skills in working with weather data to plan future vacations. Use jupyter-gmaps and the Google Places API for this part of the assignment. Note: if you having trouble displaying the maps try running jupyter nbextension enable --py gmaps in your environment and retry. Create a heat map that displays the humidity for every city from the part I of the homework. heatmap Narrow down the DataFrame to find your ideal weather condition. For example: A max temperature lower than 80 degrees but higher than 70. Wind speed less than 10 mph. Zero cloudiness. Drop any rows that don’t contain all three conditions. You want to be sure the weather is ideal. Note: Feel free to adjust to your specifications but be sure to limit the number of rows returned by your API requests to a reasonable number. Using Google Places API to find the first hotel for each city located within 5000 meters of your coordinates. Plot the hotels on top of the humidity heatmap with each pin containing the Hotel Name, City, and Country. hotel map As final considerations: Create a new GitHub repository for this project called API-Challenge (note the kebab-case). Do not add to an existing repo You must complete your analysis using a Jupyter notebook. You must use the Matplotlib or Pandas plotting libraries. For Part I, you must include a written description of three observable trends based on the data. You must use proper labeling of your plots, including aspects like: Plot Titles (with date of analysis) and Axes Labels. For max intensity in the heat map, try setting it to the highest humidity found in the data set. Hints and Considerations The city data you generate is based on random coordinates as well as different query times; as such, your outputs will not be an exact match to the provided starter notebook. You may want to start this assignment by refreshing yourself on the geographic coordinate system. Next, spend the requisite time necessary to study the OpenWeatherMap API. Based on your initial study, you should be able to answer basic questions about the API: Where do you request the API key? Which Weather API in particular will you need? What URL endpoints does it expect? What JSON structure does it respond with? Before you write a line of code, you should be aiming to have a crystal clear understanding of your intended outcome. A starter code for Citipy has been provided. However, if you’re craving an extra challenge, push yourself to learn how it works: citipy Python library. Before you try to incorporate the library into your analysis, start by creating simple test cases outside your main script to confirm that you are using it correctly. Too often, when introduced to a new library, students get bogged down by the most minor of errors – spending hours investigating their entire code – when, in fact, a simple and focused test would have shown their basic utilization of the library was wrong from the start. Don’t let this be you! Part of our expectation in this challenge is that you will use critical thinking skills to understand how and why we’re recommending the tools we are. What is Citipy for? Why would you use it in conjunction with the OpenWeatherMap API? How would you do so? In building your script, pay attention to the cities you are using in your query pool. Are you getting coverage of the full gamut of latitudes and longitudes? Or are you simply choosing 500 cities concentrated in one region of the world? Even if you were a geographic genius, simply rattling 500 cities based on your human selection would create a biased dataset. Be thinking of how you should counter this. (Hint: Consider the full range of latitudes). Once you have computed the linear regression for one chart, the process will be similar for all others. As a bonus, try to create a function that will create these charts based on different parameters. Remember that each coordinate will trigger a separate call to the Google API. If you’re creating your own criteria to plan your vacation, try to reduce the results in your DataFrame to 10 or fewer cities. Lastly, remember – this is a challenging activity. Push yourself! If you complete this task, then you can safely say that you’ve gained a strong mastery of the core foundations of data analytics and it will only go better from here. Good luck!

This repository brings a python pandas solution in the education sector to analyze the city's school district data. This project will help the school board and mayor to make strategic decisions regarding future school budgets and priorities.

Repo for PANDA challenge

25th place solution for Prostate cANcer graDe Assessment (PANDA) Challenge

A major challenge involving pollution detection is to measure the average PM2.5 concentration over major metropolitan areas. One useful method for predicting PM2.5 over a relatively wide range are AOD measurements. Ensemble algorithms are more effective than linear algorithms for prediction. Pandas and scikit-learn were used for data analysis.

Smart building technology allow buildings to be more efficient, comfortable and easier to manage by connecting IOT devices, collaborate, analyze and use real time intelligence with IOT applications and solutions as part of a Smart Building platform. A challenge for the implementation of such technologies is the evaluation of the energy use efficiency under variable climatic conditions during a year. GOALS Evaluate and predict energy efficiency performance from solar energy during a year and variable weather conditions. Tools to be used: Python Pandas, Scikit-Learn, Matplotlib Evaluate in what state of the USA solar energy is more or less efficient Tools to be used: Plotly or leaflet or D3 or Tableau (To be determined...)