In previous posts, we created several CNN networks for business problems ranging from fashion design, cancer detection, and animal detection etc. In this post, we will revisit the CNN topic. …
In a previous article, we created a stacked auto-encoder model for movie rating prediction. But as we know, with its encoder part, an auto-encoder model can also help with feature extraction. So, in this article, we will continue the journey on customer clustering using auto-encoder and k-means. …
In a previous post, we attempted to detect fraud in credit card transactions using various models. Here we will turn to another popular topic - Customer Clustering. This post aims to help friends in the marketing department. As usual, it is split into 5 parts.
In previous posts, I tried to predict if a bank customer is likely to leave, OR if an app user is likely to churn or subscribe. Here I will share recent work in the human resource domain to bring some predictive power to any firm struggling to retain their employees.
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In previous posts, I tried to predict if a bank customer is likely to leave, OR if an app user is likely to churn or subscribe. Now I will share recent work in the human resource domain to bring some predictive power to any firm struggling to retain their employees.
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Time series prediction is one of the must-know techniques for any data scientist. Questions like predicting the weather, product sales, customer visit in the shopping center, or amount of inventory to maintain, etc - all about time series forecasting, making it a valuable addition to a data scientist’s skillsets.
In…
In this article, let’s walk you through a Kaggle competition regarding credit card fraud detection. A deep neural network and two machine learning models will be built to tackle the challenge and compare different model performance. Additionally, data sampling techniques will be implemented to improve the model. …
In this article, we will elaborate on ML model selection, validation, and optimization using online loan application data. What you will learn is how to create, evaluate, and optimize ML models. Specifically, we will focus on Logistic Regression, Support Vector Machine, and Random Forest. It is split into 7 parts.
In the previous article, we created a logistic regression model to predict user enrollment using app behavior data. Hopefully, you had good learning there. This post aims to improve your model building skills with new techniques and tricks based on a larger mobile app behavior data. …
In the previous article, we introduced how to perform EDA on a small app behavior dataset. Hopefully, you learned a lot there. This post aims to improve your EDA skills with a more complicated dataset and introduce new tricks. It is split into 6 parts.
1. Data review
2. Data…
