diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000..bf3aaa8 Binary files /dev/null and b/.DS_Store differ diff --git a/Final Project Presentation H+C.pptx b/Final Project Presentation H+C.pptx new file mode 100644 index 0000000..8aaae42 Binary files /dev/null and b/Final Project Presentation H+C.pptx differ diff --git a/Mini Project.pptx b/Mini Project.pptx new file mode 100644 index 0000000..29ff6d2 Binary files /dev/null and b/Mini Project.pptx differ diff --git a/Progress Presentation.pptx b/Progress Presentation.pptx new file mode 100644 index 0000000..efbe1b4 Binary files /dev/null and b/Progress Presentation.pptx differ diff --git a/Untitled Document.md b/Untitled Document.md new file mode 100644 index 0000000..29ac0fe --- /dev/null +++ b/Untitled Document.md @@ -0,0 +1,192 @@ +# Dillinger +## _The Last Markdown Editor, Ever_ + +[![N|Solid](https://cldup.com/dTxpPi9lDf.thumb.png)](https://nodesource.com/products/nsolid) + +[![Build Status](https://travis-ci.org/joemccann/dillinger.svg?branch=master)](https://travis-ci.org/joemccann/dillinger) + +Dillinger is a cloud-enabled, mobile-ready, offline-storage compatible, +AngularJS-powered HTML5 Markdown editor. + +- Type some Markdown on the left +- See HTML in the right +- ✨Magic ✨ + +## Features + +- Import a HTML file and watch it magically convert to Markdown +- Drag and drop images (requires your Dropbox account be linked) +- Import and save files from GitHub, Dropbox, Google Drive and One Drive +- Drag and drop markdown and HTML files into Dillinger +- Export documents as Markdown, HTML and PDF + +Markdown is a lightweight markup language based on the formatting conventions +that people naturally use in email. +As [John Gruber] writes on the [Markdown site][df1] + +> The overriding design goal for Markdown's +> formatting syntax is to make it as readable +> as possible. The idea is that a +> Markdown-formatted document should be +> publishable as-is, as plain text, without +> looking like it's been marked up with tags +> or formatting instructions. + +This text you see here is *actually- written in Markdown! To get a feel +for Markdown's syntax, type some text into the left window and +watch the results in the right. + +## Tech + +Dillinger uses a number of open source projects to work properly: + +- [AngularJS] - HTML enhanced for web apps! +- [Ace Editor] - awesome web-based text editor +- [markdown-it] - Markdown parser done right. Fast and easy to extend. +- [Twitter Bootstrap] - great UI boilerplate for modern web apps +- [node.js] - evented I/O for the backend +- [Express] - fast node.js network app framework [@tjholowaychuk] +- [Gulp] - the streaming build system +- [Breakdance](https://breakdance.github.io/breakdance/) - HTML +to Markdown converter +- [jQuery] - duh + +And of course Dillinger itself is open source with a [public repository][dill] + on GitHub. + +## Installation + +Dillinger requires [Node.js](https://nodejs.org/) v10+ to run. + +Install the dependencies and devDependencies and start the server. + +```sh +cd dillinger +npm i +node app +``` + +For production environments... + +```sh +npm install --production +NODE_ENV=production node app +``` + +## Plugins + +Dillinger is currently extended with the following plugins. +Instructions on how to use them in your own application are linked below. + +| Plugin | README | +| ------ | ------ | +| Dropbox | [plugins/dropbox/README.md][PlDb] | +| GitHub | [plugins/github/README.md][PlGh] | +| Google Drive | [plugins/googledrive/README.md][PlGd] | +| OneDrive | [plugins/onedrive/README.md][PlOd] | +| Medium | [plugins/medium/README.md][PlMe] | +| Google Analytics | [plugins/googleanalytics/README.md][PlGa] | + +## Development + +Want to contribute? Great! + +Dillinger uses Gulp + Webpack for fast developing. +Make a change in your file and instantaneously see your updates! + +Open your favorite Terminal and run these commands. + +First Tab: + +```sh +node app +``` + +Second Tab: + +```sh +gulp watch +``` + +(optional) Third: + +```sh +karma test +``` + +#### Building for source + +For production release: + +```sh +gulp build --prod +``` + +Generating pre-built zip archives for distribution: + +```sh +gulp build dist --prod +``` + +## Docker + +Dillinger is very easy to install and deploy in a Docker container. + +By default, the Docker will expose port 8080, so change this within the +Dockerfile if necessary. When ready, simply use the Dockerfile to +build the image. + +```sh +cd dillinger +docker build -t /dillinger:${package.json.version} . +``` + +This will create the dillinger image and pull in the necessary dependencies. +Be sure to swap out `${package.json.version}` with the actual +version of Dillinger. + +Once done, run the Docker image and map the port to whatever you wish on +your host. In this example, we simply map port 8000 of the host to +port 8080 of the Docker (or whatever port was exposed in the Dockerfile): + +```sh +docker run -d -p 8000:8080 --restart=always --cap-add=SYS_ADMIN --name=dillinger /dillinger:${package.json.version} +``` + +> Note: `--capt-add=SYS-ADMIN` is required for PDF rendering. + +Verify the deployment by navigating to your server address in +your preferred browser. + +```sh +127.0.0.1:8000 +``` + +## License + +MIT + +**Free Software, Hell Yeah!** + +[//]: # (These are reference links used in the body of this note and get stripped out when the markdown processor does its job. There is no need to format nicely because it shouldn't be seen. Thanks SO - http://stackoverflow.com/questions/4823468/store-comments-in-markdown-syntax) + + [dill]: + [git-repo-url]: + [john gruber]: + [df1]: + [markdown-it]: + [Ace Editor]: + [node.js]: + [Twitter Bootstrap]: + [jQuery]: + [@tjholowaychuk]: + [express]: + [AngularJS]: + [Gulp]: + + [PlDb]: + [PlGh]: + [PlGd]: + [PlOd]: + [PlMe]: + [PlGa]: diff --git a/idea1.md b/idea1.md new file mode 100644 index 0000000..05e833d --- /dev/null +++ b/idea1.md @@ -0,0 +1,49 @@ +Project Proposal: Local Temperature and Precipitation Trends Analysis in Cumberland County, PA + +1. Research Question + +How have average annual temperature, the frequency of extreme heat days (above 90°F), and total annual precipitation changed in Cumberland County, PA, over the past 50 years (1973–2023), and how are these trends expected to continue or accelerate in the next 30 years (2024–2054)? + +Specifically, I aim to quantify the rate of change in average annual temperature, the frequency of extreme temperature events, and shifts in precipitation patterns. This project will provide actionable insights for public health officials, local farmers, and water resource managers to adapt to these changing climate conditions. + +2. Data Source + +The primary data source for this project will be the National Oceanic and Atmospheric Administration (NOAA) Climate Data Online portal, which provides access to historical weather data for the United States, including temperature and precipitation records. + +Data Retrieval Process: The data will be accessed by querying the NOAA Climate Data Online portal, specifying Cumberland County, PA, as the location, with a time range from 1973 to 2023. Data will be downloaded in CSV format to then load into Python. + +Variables in the Dataset: + +Temperature Variables: Daily maximum temperature, daily minimum temperature, and daily average temperature. + +Precipitation Variables: Daily total precipitation and the occurrence of extreme precipitation events (e.g., days with precipitation exceeding 1 inch). + +Other Variables: Date, location, and potential weather station data + +Variables to Be Created: + +Annual/Seasonal Averages: Calculate annual and seasonal averages for temperature and precipitation. + +Trends and Anomalies: Create variables that measure the rate of change over time and identify anomalies (unusually hot or wet years, etc.). + +Extreme Event Frequency: Count the number of days per year with temperatures above 90°F and precipitation above 1 inch to identify the frequency of extreme weather events. + +3. Data Analysis and Model Specification + +Exploratory Data Analysis (EDA): + +Initial analysis will involve data cleaning and visualization. Descriptive statistics and plots (line graphs, boxplots) will be used to understand the distribution of temperature and precipitation over time. Temporal trends can be assessed through moving averages and yearly comparisons. + +Model Specification: + +To project future trends, a time series regression model will be employed (thinking of using Ordinary Least Squares regression) with temperature and precipitation as dependent variables and time (year) as the independent variable. I can also explore the use of Autoregressive Integrated Moving Average (ARIMA) models for forecasting future climate patterns. + +Implications for Stakeholders: + +The results of this analysis will provide valuable insights for local stakeholders, such as agricultural managers, public health officials, and municipal planners, to anticipate and mitigate the impacts of climate change on farming and water management. For example, identifying an increasing trend in extreme heat days could prompt local authorities to invest in heat mitigation strategies or guide farmers on crop selection. Dickinson is also very involved with climate initiatives with Carlisle and Cumberland County, some which I have been a part of before. + +Ethical, Legal, and Societal Implications: + +Ethical considerations include ensuring that the data is used in ways that accurately represent the findings. There are no significant legal barriers to using NOAA data, as it is publicly accessible. However, the societal implications of the findings, such as the potential need for adaptation strategies, will be carefully communicated to ensure that stakeholders can make informed decisions. + + diff --git a/idea2and3.md b/idea2and3.md new file mode 100644 index 0000000..bff53f8 --- /dev/null +++ b/idea2and3.md @@ -0,0 +1,198 @@ +Idea 2 + +1. Research Question: + +How do specific ingredients in skincare products correlate with their perceived effectiveness, as measured by consumer reviews and ratings? Specifically, do commonly marketed "active ingredients" like retinol, vitamin C, and hyaluronic acid result in higher consumer satisfaction and ratings compared to products without these ingredients? + +This question aims to provide insight into the actual performance of ingredients, highlighting user experiences. + + + +2. Data Source: + +Primary Data Source: + +Sephora (Scraping Reviews and Product Listings): + +Variables Collected: + +Product name, brand, price + +User ratings (average rating and number of reviews) + +Product description and ingredient lists + +Review texts (for sentiment analysis) + +"Best-seller" or "Clean at Sephora" labels (if available) + +Potential Data Source: + +CosDNA & EWG Skin Deep: + +Variables: + +Ingredient details and safety ratings + +Ingredient classifications + +Data Retrieval Process: + +Scraping Tools: I will use Python libraries to scrape product details from Sephora's website. + +Variables Created: + +Binary features for the presence of specific active ingredients (retinol: yes/no). + +Sentiment scores from reviews using Natural Language Processing. + +Price per unit (price per ounce or mL for skincare products). + + + +3. What I Will Do with the Data: + +Exploratory Data Analysis (EDA): + +Descriptive Statistics: Summarize the distributions of prices, ratings, and key ingredient presence across product categories. + +Correlation Analysis: Explore the relationships between the presence of active ingredients and product ratings, controlling for price and brand. + +Model Specification: + +Model Choice: + +Sentiment Analysis: + +Purpose: Convert review text into sentiment scores to assess overall user satisfaction. + +Clustering Models: + +Model: K-Means Clustering or Hierarchical Clustering to group ingredients based on their impact on ratings and effectiveness. + +Purpose: Identify patterns and groupings among ingredients to analyze their collective influence. + +Regression Models: + +Model: Linear Regression to model the relationship between ingredient presence and ratings. + +Purpose: Predict how individual ingredients impact ratings, controlling for price and product category. + +Stakeholder Implications: + +For consumers: Provide data-backed insights into which ingredients actually improve product satisfaction. + +For brands: Help product developers focus on the most effective ingredients. + +For dermatologists: Insights could help guide patient recommendations based on user experiences. + +Ethical, Legal, and Societal Implications: + +Ethical Considerations: Ensure transparency in the analysis by avoiding biased interpretations of ingredient effectiveness. Address the influence of marketing in reviews. + +Legal Considerations: Scraping terms of use for websites must be reviewed, ensuring no violation of policies. + +Societal Implications: The project can improve consumer decision-making, promoting ingredient awareness and reducing the impact of misleading marketing claims. + + + +1. Research Question: + +How do color and pattern trends in the fashion industry evolve over time, and can historical trend data be used to predict future fashion trends? Specifically, what role do recurring color palettes and patterns play in the popularity of fashion collections in different seasons? + +This project aims to analyze the evolution of fashion trends through color and pattern analysis and provide insights for fashion brands on which aesthetics are likely to become popular in future seasons. + + + +2. Data Source: + +Primary Data Source: + +Fashion Runway Archives: Publicly available images from fashion weeks (e.g., New York, Paris, Milan), accessible via online databases or fashion blogs. + +Variables Collected: + +Image Data: Photos of fashion collections from different designers across various seasons. + +Timestamp: The season and year of the fashion show (e.g., Spring/Summer 2023). + +Designer Name: The designer or fashion house associated with the collection. + +Clothing Type: The category of clothing (e.g., dresses, outerwear, accessories). + +Color and Pattern: Identified using image processing techniques (e.g., dominant colors, pattern types). + +Potential Data Source: + +Online Fashion Retailers: Websites like Net-a-Porter or Farfetch that display collections from top designers. + +Variables Collected: + +Product Descriptions: Text descriptions that may highlight color or pattern features. + +Sales Data (if available): Pricing and sales metrics that could link color trends to commercial success. + +Data Retrieval Process: + +Image Data Collection: Use python web scraping tools to collect fashion week images from public sources like Vogue’s fashion archives. + +Image Processing Tools: Apply computer vision libraries (e.g., OpenCV, PIL) to extract color palettes and patterns from each image. + +Variables Created: + +Dominant Colors: Extracted from images using clustering techniques (K-Means) to identify the most prominent colors in each collection. + +Pattern Detection: Identify repeating patterns (stripes, florals, polka dots) using feature extraction methods such as texture analysis and edge detection. + + + +3. What I Will Do with the Data: + +Exploratory Data Analysis (EDA): + +Descriptive Statistics: Analyze the frequency of different colors and patterns over time, grouped by season and designer. + +Trend Analysis: Identify the most recurring colors and patterns across multiple seasons and designers, and analyze the correlation between these trends and the success of a collection (if sales data is available). + +Model Specification: + +Model Choice: + +Time Series Forecasting: + +Purpose: To predict future fashion trends by modeling the historical popularity of colors and patterns over time. + +Independent Variables: Color, pattern, designer, season, and clothing type. + +Dependent Variable: Trend popularity (measured by frequency or sales performance). + +Clustering Models: + +K-Means Clustering: + +Purpose: To group fashion collections by similar color palettes and patterns, helping to uncover underlying aesthetic trends. + +Evaluation Metrics: Root Mean Squared Error (RMSE) for time series forecasting and silhouette score for clustering to evaluate the model’s performance. + + + +4. Stakeholder Implications: + +For Fashion Designers: The analysis can inform future collection designs by identifying which color and pattern combinations are likely to resonate with consumers in upcoming seasons. + +For Retailers: Retailers can use trend predictions to stock their inventory with trending colors and patterns ahead of time, optimizing sales and reducing unsold stock. + +For Consumers: Trend analysis can help fashion-conscious consumers anticipate what styles will be in demand, allowing them to stay ahead of the curve. + + + +5. Ethical, Legal, and Societal Implications: + +Ethical Considerations: Ensure fair representation of fashion across diverse cultures and designers, avoiding bias toward certain trends based on geographic or demographic factors. + +Legal Considerations: Ensure compliance with copyright laws when scraping and analyzing fashion show images. Review terms of use for image databases and retailer websites. + +Societal Implications: The project could influence the sustainability of fashion by promoting timeless color and pattern choices, reducing the rapid turnover of trends that lead to excessive consumption and waste. + + + diff --git a/test.md b/test.md new file mode 100644 index 0000000..29ac0fe --- /dev/null +++ b/test.md @@ -0,0 +1,192 @@ +# Dillinger +## _The Last Markdown Editor, Ever_ + +[![N|Solid](https://cldup.com/dTxpPi9lDf.thumb.png)](https://nodesource.com/products/nsolid) + +[![Build Status](https://travis-ci.org/joemccann/dillinger.svg?branch=master)](https://travis-ci.org/joemccann/dillinger) + +Dillinger is a cloud-enabled, mobile-ready, offline-storage compatible, +AngularJS-powered HTML5 Markdown editor. + +- Type some Markdown on the left +- See HTML in the right +- ✨Magic ✨ + +## Features + +- Import a HTML file and watch it magically convert to Markdown +- Drag and drop images (requires your Dropbox account be linked) +- Import and save files from GitHub, Dropbox, Google Drive and One Drive +- Drag and drop markdown and HTML files into Dillinger +- Export documents as Markdown, HTML and PDF + +Markdown is a lightweight markup language based on the formatting conventions +that people naturally use in email. +As [John Gruber] writes on the [Markdown site][df1] + +> The overriding design goal for Markdown's +> formatting syntax is to make it as readable +> as possible. The idea is that a +> Markdown-formatted document should be +> publishable as-is, as plain text, without +> looking like it's been marked up with tags +> or formatting instructions. + +This text you see here is *actually- written in Markdown! To get a feel +for Markdown's syntax, type some text into the left window and +watch the results in the right. + +## Tech + +Dillinger uses a number of open source projects to work properly: + +- [AngularJS] - HTML enhanced for web apps! +- [Ace Editor] - awesome web-based text editor +- [markdown-it] - Markdown parser done right. Fast and easy to extend. +- [Twitter Bootstrap] - great UI boilerplate for modern web apps +- [node.js] - evented I/O for the backend +- [Express] - fast node.js network app framework [@tjholowaychuk] +- [Gulp] - the streaming build system +- [Breakdance](https://breakdance.github.io/breakdance/) - HTML +to Markdown converter +- [jQuery] - duh + +And of course Dillinger itself is open source with a [public repository][dill] + on GitHub. + +## Installation + +Dillinger requires [Node.js](https://nodejs.org/) v10+ to run. + +Install the dependencies and devDependencies and start the server. + +```sh +cd dillinger +npm i +node app +``` + +For production environments... + +```sh +npm install --production +NODE_ENV=production node app +``` + +## Plugins + +Dillinger is currently extended with the following plugins. +Instructions on how to use them in your own application are linked below. + +| Plugin | README | +| ------ | ------ | +| Dropbox | [plugins/dropbox/README.md][PlDb] | +| GitHub | [plugins/github/README.md][PlGh] | +| Google Drive | [plugins/googledrive/README.md][PlGd] | +| OneDrive | [plugins/onedrive/README.md][PlOd] | +| Medium | [plugins/medium/README.md][PlMe] | +| Google Analytics | [plugins/googleanalytics/README.md][PlGa] | + +## Development + +Want to contribute? Great! + +Dillinger uses Gulp + Webpack for fast developing. +Make a change in your file and instantaneously see your updates! + +Open your favorite Terminal and run these commands. + +First Tab: + +```sh +node app +``` + +Second Tab: + +```sh +gulp watch +``` + +(optional) Third: + +```sh +karma test +``` + +#### Building for source + +For production release: + +```sh +gulp build --prod +``` + +Generating pre-built zip archives for distribution: + +```sh +gulp build dist --prod +``` + +## Docker + +Dillinger is very easy to install and deploy in a Docker container. + +By default, the Docker will expose port 8080, so change this within the +Dockerfile if necessary. When ready, simply use the Dockerfile to +build the image. + +```sh +cd dillinger +docker build -t /dillinger:${package.json.version} . +``` + +This will create the dillinger image and pull in the necessary dependencies. +Be sure to swap out `${package.json.version}` with the actual +version of Dillinger. + +Once done, run the Docker image and map the port to whatever you wish on +your host. In this example, we simply map port 8000 of the host to +port 8080 of the Docker (or whatever port was exposed in the Dockerfile): + +```sh +docker run -d -p 8000:8080 --restart=always --cap-add=SYS_ADMIN --name=dillinger /dillinger:${package.json.version} +``` + +> Note: `--capt-add=SYS-ADMIN` is required for PDF rendering. + +Verify the deployment by navigating to your server address in +your preferred browser. + +```sh +127.0.0.1:8000 +``` + +## License + +MIT + +**Free Software, Hell Yeah!** + +[//]: # (These are reference links used in the body of this note and get stripped out when the markdown processor does its job. There is no need to format nicely because it shouldn't be seen. Thanks SO - http://stackoverflow.com/questions/4823468/store-comments-in-markdown-syntax) + + [dill]: + [git-repo-url]: + [john gruber]: + [df1]: + [markdown-it]: + [Ace Editor]: + [node.js]: + [Twitter Bootstrap]: + [jQuery]: + [@tjholowaychuk]: + [express]: + [AngularJS]: + [Gulp]: + + [PlDb]: + [PlGh]: + [PlGd]: + [PlOd]: + [PlMe]: + [PlGa]: