Dating website optimizer
One problem I noticed, was I swiped left for about 80% of the profiles. It would difficult to extract information from such a high variation of images.As a result, I had about 8000 in dislikes and 2000 in the likes folder. Because I have such few images for the likes folder, the date-ta miner won’t be well-trained to know what I like. To fix this problem, I found images on google of people I found attractive. To solve this problem, I used a Haars Cascade Classifier Algorithm to extract the faces from images and then saved it.Once I attain a match, the AI will automatically send a message to the matchee.While this doesn’t give me a competitive advantage in photos, this does give me an advantage in swipe volume & initial message.Image optimization is an art that not many people master.There are many good image editing tools that allow us to get the best visual result for a certain file size but "under the hood" a lot more optimization can be done.To solve this swipe volume problem, I decided to build an AI that automates tinder called: THE DATE-A MINER.The DATE-A MINER is an artificial intelligence that learns the dating profiles I like.
It uses image format specific non-lossy image optimization tools to squeeze the last bytes out of your images - without changing their look or visual quality.Because my classification problem was extremely detailed & subjective, I needed an algorithm that could extract a large enough amount of features to detect a difference between the profiles I liked and disliked.A c NN was also built for image classification problems.Then I scraped these images and used them within my dataset. The Classifier, essentially uses multiple positive/negative rectangles.Now that I have the images, there are a number of problems. Passes it through a pre-trained Ada Boost model to detect the likely facial dimensions: The Algorithm failed to detect the faces for about 70% of the data. To model this data, I used a Convolutional Neural Network.