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INFT216 - Data Science Assignment - Decision Trees: Will an online shopper make a purchase?

This assignment uses a sample of real data from an online store.

In physical retailing, a salesperson can offer a range of customized alternatives to shoppers based on experience or customer behaviour. This has an important influence on the effective use of time, purchase conversion rates, and sales figures.

The dataset was formed so that each session would belong to a different user in a 1-year period to avoid any tendency to a specific campaign, special day, user profile, or period.

The objective is to predict whether a customer will make a purchase in this session. Every time a web page is served to the user, the online store will make a prediction of how likely it is that the customer will purchase. The store can use this probability to customize buying offers, provide discounts, offer purchase advice, etc.

I have loaded this data into an SQL database (to make sure you get some practice using SQL!). The data is located at BRUCEDBA.ONLINEPURCHASES, and you should access it directly from R following the same procedure we used in workshops.

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Feature Description -

Feature name

Feature description

ADMINISTRATIVE

Number of pages visited by the visitor about account management

ADMINISTRATIVE_DURATION

Total amount of time (in seconds) spent by the visitor on account management related pages

INFORMATIONAL

Number of pages visited by the visitor about Web site, communication and address information of the shopping site

INFORMATIONAL_DURATION

Total amount of time (in seconds) spent by the visitor on informational pages

PRODUCT_RELATED

Number of pages visited by visitor about product related pages

PRODUCT_RELATED_DURATION

Total amount of time (in seconds) spent by the visitor on product related pages

BOUNCE_RATE

Average bounce rate value of the pages visited by the visitor

EXIT_RATE

Average exit rate value of the pages visited by the visitor

PAGE_VALUE

Average page value of the pages visited by the visitor

SPECIAL_DAY

Closeness of the site visiting time to a special day

OPERATINGSYSTEMS

Operating system of the visitor

BROWSER

Browser of the visitor

REGION

Geographic region from which the session has been started by the visitor

TRAFFICTYPE

Traffic source by which the visitor has arrived at the Web site (e.g., banner, SMS, direct)

VISITORTYPE

Visitor type as "New Visitor," "Returning Visitor," and "Other"

WEEKEND

Boolean value indicating whether the date of the visit is weekend

MONTH

Month value of the visit date

REVENUE

Class label indicating whether the visit has been finalized with a transaction

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Notes on Selected Features -

ADMINISTRATIVE, ADMINISTRATIVE_DURATION, INFORMATIONAL, INFORMATIONAL_DURATION, PRODUCT_RELATED, PRODUCT_RELATED_DURATION

i. These features represent the number of different types of pages visited by the visitor in that session and the 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

BOUNCE_RATE, EXIT_RATE, PAGE_VALUE

i. These features represent the metrics measured by "Google Analytics" for each page in the ecommerce 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.

SPECIAL_DAY

i. This 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 ecommerce such as the duration between the order date and delivery date. For example, for Valentine'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 1on February 8.

OPERATINGSYSTEMS, BROWSER, REGION, TRAFFICTYPE, VISITORTYPE

i. Attributes of specific user session.

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Task - Your goal is to create a decision tree which can predict class membership of the "Revenue" variable. Clearly, the better the tree prediction, the happier management will be... and the higher your mark!

Deliverables: Your final deliverables will be 2 PDF files, both produced by the same .Rmd script (with different code chunk options).

I - All code and results shown (like you would share with a colleague on the Data Science team)

II - Only show those things necessary to help support management decision making (this is the one you send to management!)

Please submit only these two PDF's through iLearn. Please don't submit R code or your model.

Helpful hints -

i. Read the document on how to create a management report.

ii. It's a good idea (at least to start off with) to use the template - customize it as much as you wish.

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Decision trees are a powerful machine learning algorithm that can be used for both classification and regression tasks. They are easy to understand and interpret, making them a popular choice for beginners and experts alike.

Decision trees work by building a tree-like structure that represents the different decision points and possible outcomes of a given problem. Each node in the tree represents a decision, and each branch represents an outcome. The leaves of the tree represent the final predictions.

To train a decision tree, you provide it with a dataset of labeled examples. The decision tree will then learn to split the data into different subsets based on the values of the features. This process is repeated recursively until the data is completely split into pure subsets, or until a certain stopping criterion is met.

Once the decision tree is trained, it can be used to make predictions for new data points. To do this, the new data point is passed through the tree, starting at the root node. At each node, the decision tree will compare the value of the feature at that node to the values of the training data. The new data point will then be sent down the branch that corresponds to the closest value. This process is repeated until the new data point reaches a leaf node. The prediction of the decision tree is the value of the target variable at that leaf node.

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