Web Traffic Prediction

A study published in the Journal of Business Research found that an AI-based web traffic prediction system could predict web traffic patterns with an accuracy of around 80%.

Another study published in the Journal of Computer Science found that AI-based web traffic prediction systems could improve website performance by up to 25% compared to traditional methods.

AI-based web traffic prediction is using machine learning algorithms to predict future web traffic patterns. This can include analyzing historical web traffic data, such as page views, clicks, and bounce rates, as well as external factors, such as weather, social media trends, and search engine rankings. Web traffic prediction can optimize website design and content, improve search engine optimization (SEO), and plan for server capacity and resources. AI can improve the accuracy of web traffic forecasting and website optimization, leading to increased website performance and user engagement.

An AI-based web traffic prediction system can provide several benefits for businesses looking to optimize their website:

• Improved website performance: AI-based web traffic prediction can help businesses to optimize the website design and content, which can lead to increased website performance and user engagement.
• Increased revenue: By predicting future web traffic patterns and optimizing website design and content, businesses can increase revenue from advertising and e-commerce sales.
• Better SEO: AI-based web traffic prediction can help businesses to improve their search engine optimization (SEO) strategies, leading to increased visibility and more web traffic.
• Resource planning: AI-based web traffic prediction can help businesses to plan for server capacity and resources, ensuring that their website can handle increased traffic during peak periods.
• Scalability: AI web traffic prediction can be used for websites of all sizes and can be scaled up or down as needed.
• Personalization: AI-based web traffic prediction can also be used to personalize the web experience for individual users, increasing user engagement and revenue.

Overall; an AI-based web traffic prediction system can provide valuable tools for businesses looking to optimize their website, increase revenue, improve SEO, plan resources and personalize the web experience for their users.

An e-commerce company wanted to optimize its website and improve its customer experience. With the help of Attri’s Blueprint, they were able to effortlessly analyze internal components, such as historical web traffic data, such as page views, clicks, and bounce rates, as well as external factors, such as weather, social media trends, and search engine rankings.

• 12% increase in sales
• 72% reduction in bounce rates,
• 8% reduction in resource expenditure

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Understanding the Data

The goal of this predictive system is to forecast what a particular page of a particular website might experience during a given time window. We use Google’s Public Dataset on Kaggle. The dataset consists of time-series samples consisting of the page visited, and the type of device used. We’ll be using around 140K samples for training purposes. 

Input Features

Page Name

Number of visits on {Day 1}

Number of visits on {Day 2}

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.

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Number of visits on {Day n}

Setting up the training

After performing our standard data pre-preprocessing, we do some feature engineering to help build better context around the data - helping the model understand the trends better. The idea is to get the model to predict the traffic corresponding to each page in the dataset, which implies that we’re going to model ‘n’ number of time series. We’re going to provide the model with Yearly, and weekly seasonalities to help with the modeling process. Depending on the nature of the data and other complexities involved, sometimes, traditional machine learning might not be the best solution which is why we equip our blueprint with statistical models such as ARIMA, SARIMAX, and Prophet along with the latest advancements in TimeSeries, and Temporal Fusion Transformers.

Customization made easy

When it comes to model training, we train various models spanning something as simple as logistic regression to something as sophisticated as a neural network, all while varying the hyperparameters of the models, so you don't have to worry if you are using the right model for the job. We operate in a completely transparent manner making sure that there are no black boxes throughout the blueprint. We make use of open-source libraries and frameworks so that you can easily modify, replace and optimize for much more customization should you need it. Thanks to our transparent approach, you can get your hands dirty by playing around with the hyperparameters should you feel the need for experimentation. The performance of the models corresponding to different sets of hyperparameters is logged so you don’t need to make a note every time you intend to tweak the models. 

Evaluation Metrics

There’re multiple evaluation metrics for regression models. Some most common metrics are:

Mean Absolute Error [MAE]

This is the average difference between the actual and the predicted values. [Lower the better]

Mean Absolute Percentage Error [MAPE]

Explains the error in the accuracy of the forecasting. [Lower the better]

Coefficient of determination[R2]

Explains the quality of the predictions on a scale of 0 to 1 with 1 being the perfect score. [Higher the better]

The performance can further be improved by tweaking the hyperparameters of the model or by more feature engineering. Note that the performance of the model, or any Machine Learning model for that matter, is dependent on the nature of the dataset being used, it is important to have potent data to produce good results.

The Last Mile

We now have the model ready, and the next big step is to take it live. Model Monitoring and Responsibility are things that are often overlooked. Models in production come with their own set of challenges some noteworthy ones are model drift and data drift. It is also important to make sure that the models are responsible, that is, the prediction of the models shouldn’t biased to sensitive elements such as race, and gender. Our blueprint is further integrated with Censius- a platform for end-to-end AI Observability. 

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