A data science blog documenting learning, projects, concepts, and how-tos of this incredible field.
TensorFlow is an open-source machine learning library with a specific focus on deep learning and neural network. There are endless uses of TensorFlow, but let’s see how we can use it for SPAM detection by utilizing its natural language processing capabilities.
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A Market Basket Analysis (MBA) is an industry-standard in retails to study consumer’s purchasing habits. It helps the retail industry to identify what items are bought together frequently. The whole mechanism is to mine the combinations or associations of items using any retail store’s transaction database.
Ever wondered why there is always chocolate syrup next to icecream stalls in a supermarket? or why you will always find spice rubs next to the meat section selling steaks? These are not coincidences; it is a very well thought out strategy based on Market Basket Analysis.
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This project is inspired by a famous Kaggle competition called House Prices: Advanced Regression Techniques. The original project on Kaggle is based on the Boston Housing dataset and is an ideal project for newbies to hone their skills on.
The original project on Kaggle gives you full opportunity to practice data cleaning, exploratory analysis, and modeling. However, one aspect of the data science project lifecycle was missing, i.e., data scavenging and extraction. The data is already extracted for you, and you don’t know how the information was gathered.
My objective was to achieve the same goals of the original project, but by doing so, using my dataset. Instead of Boston housing, I started looking into the Dubai real estate market and opted to use one of the prominent real estate property portals for my data extraction.
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