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1. Predicting credit card spend
This project is based on a regression problem. The task is to train a machine learning model that can predict credit card spending based on historical data. This model can help banking industries to decide credit card limit based on user’s past experience.
This project has 15+ columns to find the best features out of them. In this way, you will also learn different features elimination and selection techniques.
Finally, we will use bagging and boosting mechanisms to find a better model for our data.
2. Walmart sales forecasting
This project is another real-time industrial task. The main goal is to forecast the future sales of different items. This project can help companies to maintain their supply depends on future sales.
Here, we have the interval level data for sales. We can try ARIMA, SARIMA, Holts, and other prediction models to forecast sales for the upcoming months.
3. Network intrusion detection
This project is another exciting classification task with 100+ features to train a classification model to classify the network intrusion type.
The main task here is to try different features elimination and features selection techniques to reduce the number of features. And, finally, we have to come up with some important features that affect the target variable.
In the end, we can try a set of classification models to find the best suitable model for our data.
4. Segmenting credit card users
This project is from the segmentation area. The task is to make segments of different users who are having the same characteristics. Industries use these segments to target their users with a set of campaigns.
This project will use the KMeans clustering to define different clusters based on the silhouette scores of different clusters.
In the end, we will get an excel file with different segments of users to target our marketing strategies.
5. Analyzing online job posts
This project is another exciting text analysis task. Here, you will learn different ways to deal with data cleaning steps. You will learn about different ways to convert text data into numeric vectors that help our machine to understand the text data.
We will also work with the text classification model followed by word cloud representation of the text data for summarization.
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