Valentina Product - Moozonian

GitHub Repo https://github.com/ValentinaBacce/crud_django_productos_VALENTINA

ValentinaBacce/crud_django_productos_VALENTINA

VALENTINA BACCELLIERI 30865954 SUMATIVA 2

GitHub Repo https://github.com/FlorR566/lady-valentina-backend

FlorR566/lady-valentina-backend

Backend service for Lady Valentina store, managing product catalog and database persistence. Built with Node.js, Express, and MongoDB Atlas.

GitHub Repo https://github.com/giorgosrigas/Online-shoppers-Intention-Predictions-Supervised-Machine-Learning

giorgosrigas/Online-shoppers-Intention-Predictions-Supervised-Machine-Learning

The data used in this analysis is an Online Shoppers Purchasing Intention data set provided on the UC Irvine’s Machine Learning Repository. The primary purpose of the data set is to predict the purchasing intentions of a visitor to this particular store’s website. Attribute Information: The dataset consists of 10 numerical and 8 categorical attributes. The 'Revenue' attribute can be used as the class label. Of the 12,330 sessions in the dataset, 84.5% (10,422) were negative class samples that did not end with shopping, and the rest (1908) were positive class samples ending with shopping. "Administrative", "Administrative Duration", "Informational", "Informational Duration", "Product Related" and "Product Related Duration" represent the number of different types of pages visited by the visitor in that session and total time spent in each of these page categories. The values of these features are derived from the URL information of the pages visited by the user and updated in real time when a user takes an action, e.g. moving from one page to another. The "Bounce Rate", "Exit Rate" and "Page Value" features represent the metrics measured by "Google Analytics" for each page in the e-commerce site. The value of "Bounce Rate" feature for a web page refers to the percentage of visitors who enter the site from that page and then leave ("bounce") without triggering any other requests to the analytics server during that session. The value of "Exit Rate" feature for a specific web page is calculated as for all pageviews to the page, the percentage that were the last in the session. The "Page Value" feature represents the average value for a web page that a user visited before completing an e-commerce transaction. The "Special Day" feature indicates the closeness of the site visiting time to a specific special day (e.g. Mother’s Day, Valentine's Day) in which the sessions are more likely to be finalized with transaction. The value of this attribute is determined by considering the dynamics of e-commerce such as the duration between the order date and delivery date. For example, for Valentina’s day, this value takes a nonzero value between February 2 and February 12, zero before and after this date unless it is close to another special day, and its maximum value of 1 on February 8. The dataset also includes operating system, browser, region, traffic type, visitor type as returning or new visitor, a Boolean value indicating whether the date of the visit is weekend, and month of the year.

GitHub Repo https://github.com/nvrpicurnose/valentinas-ecommerce-store

nvrpicurnose/valentinas-ecommerce-store

Online eCommerce Store for small business, originally developed for Valentinas (Jeans&Apparel). Unique features of this website include a Fashion Model Submission form where customers who buy Jeans can submit their own pictures to be shown on the front page as a “model”. Social media driven advertising is used for “models” to link their own photos to friends. Small business owners can edit the layout of their products on the site without developer aid, via a simple custom built GUI made with the help of Ruby Gem acts_as_list. Auto-email feature is triggered whenever certain events occur. Admins and users are notified via ActionMailer Gmail. Payments are handled through ActiveMerchant via. Paypal.

GitHub Repo https://github.com/VerloCC/Valentina-s-Mobile-Production-WIP

VerloCC/Valentina-s-Mobile-Production-WIP

No repository description available.

GitHub Repo https://github.com/Valen2104/product-valentina

Valen2104/product-valentina

No repository description available.

GitHub Repo https://github.com/stacykeago/Predicting-customer-ad-clicks-on-a-blog-page-K-Means-clustering-vs-Hierarchical-clustering

stacykeago/Predicting-customer-ad-clicks-on-a-blog-page-K-Means-clustering-vs-Hierarchical-clustering

Part 1: Research Question This section of the assessment covers unsupervised learning with R. We will revisit our last week's case study and using the learnings and the given datasets. We will be tasked to create a supervised learning model to help identify which individuals are most likely to click on the ads in the blog. Note that you will be required to include your last week's IP insights thus you can add a modeling section to your last week's submission submit it. Part 2: Research Question This section of the assessment covers unsupervised learning with R. Kira Plastinina is a Russian brand that is sold through a defunct chain of retail stores in Russia, Ukraine, Kazakhstan, Belarus, China, Philippines, and Armenia. The brand’s Sales and Marketing team would like to understand their customer’s behavior from data that they have collected over the past year. More specifically, they would like to learn the characteristics of customer groups. Perform clustering stating insights drawn from your analysis and visualizations. Upon implementation, provide comparisons between the approaches learned this week i.e. K-Means clustering vs Hierarchical clustering highlighting the strengths and limitations of each approach in the context of your analysis. Your findings should help inform the team in formulating the marketing and sales strategies of the brand. You will create a Markdown which will comprise the following sections. Problem Definition Data Sourcing Check the Data Perform Data Cleaning Perform Exploratory Data Analysis (Univariate, Bivariate & Multivariate) Implement the Solution Challenge the Solution Follow up Questions The dataset for this Independent project can be found here [http://bit.ly/EcommerceCustomersDataset]. The dataset consists of 10 numerical and 8 categorical attributes. The 'Revenue' attribute can be used as the class label. "Administrative", "Administrative Duration", "Informational", "Informational Duration", "Product Related" and "Product Related Duration" represents the number of different types of pages visited by the visitor in that session and total time spent in each of these page categories. The values of these features are derived from the URL information of the pages visited by the user and updated in real-time when a user takes an action, e.g. moving from one page to another. The "Bounce Rate", "Exit Rate" and "Page Value" features represent the metrics measured by "Google Analytics" for each page in the e-commerce site. The value of the "Bounce Rate" feature for a web page refers to the percentage of visitors who enter the site from that page and then leave ("bounce") without triggering any other requests to the analytics server during that session. The value of the "Exit Rate" feature for a specific web page is calculated as for all pageviews to the page, the percentage that was the last in the session. The "Page Value" feature represents the average value for a web page that a user visited before completing an e-commerce transaction. The "Special Day" feature indicates the closeness of the site visiting time to a specific special day (e.g. Mother’s Day, Valentine's Day) in which the sessions are more likely to be finalized with the transaction. The value of this attribute is determined by considering the dynamics of e-commerce such as the duration between the order date and delivery date. For example, for Valentina’s day, this value takes a nonzero value between February 2 and February 12, zero before and after this date unless it is close to another special day, and its maximum value of 1 on February 8. The dataset also includes the operating system, browser, region, traffic type, visitor type as returning or new visitor, a Boolean value indicating whether the date of the visit is weekend, and month of the year.