Here are some of the most interesting projects we have been involved in so far.
Computing Keyword Similarity
The goal of computing similarity is often to identify words or phrases that are related in meaning, and can be useful in a wide range of natural language processing tasks such as information retrieval, text classification, and machine translation. For instance, one use case that our team developed was create an NLP based algorithm to group phrases by the location specified.
Predicting Search Volume of Different Products sold online using their Rank
The idea behind this approach is that products with higher search volumes are likely to have higher sales and better rankings. By analyzing historical data on search volumes and product rankings, machine learning algorithms can identify patterns and make predictions about the relative rankings of different products based on their search volumes.
This kind of analysis can be useful for online sellers who want to optimize their product listings and improve their sales performance. By identifying the search terms and keywords that are most commonly associated with high-performing products, sellers can improve their product descriptions, titles, and other metadata to improve their search rankings and attract more customers.
Feasibility Studies on building different Machine Learning Applications on AWS
The analysis may include factors such as the availability of data, the computational resources required to train and deploy machine learning models, the cost of using AWS services, and the technical expertise required to build and maintain the applications.
The goal of these studies is to assess the feasibility of building machine learning applications on AWS and to identify potential challenges and opportunities for optimization. This can help organizations to make informed decisions about whether or not to pursue machine learning initiatives on AWS, and if so, what approach to take.
Predicting Click Through Rate by the Number of Impressions using Google data
By collecting data on the number of impressions and click-throughs for a particular ad or group of ads, machine learning algorithms can be used to identify patterns and predict the CTR based on the number of impressions. This analysis can be used to optimize ad campaigns and improve the performance of ads by predicting the likelihood of a user clicking on an ad based on the number of times it has been displayed.
The prediction of CTR can be useful in many ways, such as improving ad targeting, selecting optimal bids and budgets, and measuring the overall performance of ad campaigns. By analyzing the relationship between impressions and clicks, advertisers can identify which ads are performing well and adjust their strategy accordingly.