Week 8 ‘The End’ — Career Path Predictor

Hello everyone from our last week in our project! While we are proud of completing our project, we are also experiencing the bitter pain of writing the last blog post of this project. Of course, we can’t wait to share the final version of the project with you, let’s take a look at what we did this week.

Although it was the Logistic Regression model that gave the highest result from the different algorithms we tried last week, considering that it gave a very close result with the Gradient Boosting model, we applied parameter selection with GridSearchCV for the Gradient Boosting model as well. And we observed that the optimal parameters we obtained from here gave better results than the Logistic Regression model. Thus, the model we will use in our main project has been finalized. Then, we observed the effects of oversampling and undersampling applications on the accuracy of the bo model. Although we tried with different strategies, we gave up on these applications when the accuracy values we obtained were lower.

In our other project, where we used a smaller dataset that will offer suggestions to our users in 3 different more specific areas, we chose the AdaBoost model. Although we tried other algorithms, the AdaBoost model gave the best results. We saved this model and made it ready to use in our main project.

Then we moved on to the System Creating part, which is one of the most enjoyable parts of the project. Here, we ask our user 36 different questions and send the answers we get from these questions to both of our models. At the end of the test, we send the results returned from our models to our users.

We had a lot of fun and learned during the whole project. I hope we were able to help everyone who showed interest in our project during this process, and we were able to make you have fun with us. Thank you for your interest and we wish you days with AI.

- Melike Nur Dulkadir

- Sare Naz Ersoy