The goal is to estimate the popularity of music tracks based on their audio features.
Predicting the popularity of music can help music streaming platforms understand user preferences, optimize playlists and enhance recommendation systems to improve
user engagement and satisfaction.
The goal is to create a recommendation system that can suggest relevant songs to user based on their musical interest, known and liked songs, or general preferences.
A recommendation system analyzes the characteristics of songs, such as genre, tempo, energy, and other audio features, and groups them into clusters. These clusters represent groups of songs that share similar attributes.
The goal is to create a recommendation system that can suggest relevant songs to user based on their musical interest, known and liked songs, or general preferences by using two different approaches, content-based and hybrid!
The content-based approach is based on the idea that a user's preferences can be predicted based on their previous interactions with items, such as their viewing and purchasing history!
The hybrid approach aims to provide more personalized and relevant recommendations by considering both the content similarity of songs and their weighted popularity.
Make recommendations based on similar users
Find the products that the top similar users have purchased.
Filter out the products that the target user has already purchased.
Recommend the remaining products to the target user, prioritizing those that were purchased by more of their similar users or had higher purchase amounts.
Predict if a user will purchase a product or not!
In the insurance industry, predicting the likelihood of claims is critical for risk assessment and policy pricing. However, insurance claims datasets often exhibit class imbalance, with non-claims outnumbering actual claims.
Building a Game Recommendation System with Steam Platform Data!
The goal is to develop a dynamic pricing model that optimizes the prices of items to maximize revenue while remaining competitive in the market.
Key here is to unravel how our pricing strategies have influenced sales and revenue historically, and how competitor prices impact our performance.
Clustering credit cards based on their buying habits, credit limits, and many other financial factors!
Netflix Content Strategy Analysis, we need data based on content titles, type (show or movie), genre, language, and release details (date, day of the week, season) to understand timing and content performance!
The main goal of this project is building and comparing various time series and machine learning models to predict Bitcoin prices, aiming to identify the most accurate methods for forecasting future cryptocurrency trends.
The main goal of this project is identifying unusual or unexpected patterns within transactions or related activities.