Let's try it #3 : Linear Regression Model Output/Observation
Linear Regression Model Output/Observation
by Ashur Baroutta Following up and concluding our Let's Try It series. We've installed the software, we've imported our data set and selected for certain attributes. Now we need to create the learning model and start looking at results and thinking about the information we can gain from the results.
We need to first create the model within the program. Given the software packages we've downloaded and imported, coding has mostly been done for us, we just need to implement the provided methods. Using the software described in the series, the code looks as follows.
With the learning model created, we now want to fit the data within the model to the portion of the data set, that we previously split in the last blog, and score the results. To do this we implement the "fit" and "score" functions, provided already within the library, as follows.
All that is left is running the programing and printing "accuracy" to find our results. Let's try it.
As we can see here, the model predicted a students final grade with near 87% accuracy. Results vary a few percent as you continue running the program and the model learns, though what's interesting is now with this experience we can try removing or training on different parameters and seeing which make the biggest impact (be it positively and negatively), and we can apply it to different data sets to see if results are similar or continue improving.
This experience was fun and encourages me to continue learning more about learning models and how to use them to produce something of value in the future. Thank you for your time and I hope to continue being apart of the program in the future.
Cited
https://archive.ics.uci.edu/ml/datasets/Student+Performance
With the learning model created, we now want to fit the data within the model to the portion of the data set, that we previously split in the last blog, and score the results. To do this we implement the "fit" and "score" functions, provided already within the library, as follows.
All that is left is running the programing and printing "accuracy" to find our results. Let's try it.
This experience was fun and encourages me to continue learning more about learning models and how to use them to produce something of value in the future. Thank you for your time and I hope to continue being apart of the program in the future.
Cited
https://archive.ics.uci.edu/ml/datasets/Student+Performance
Ashur,
ReplyDeleteI really commend you on your work. These last few assignments are widely applicable to many different things, and really force you to get involved. I love this sort of thing. Hope your holiday season was restful.
Best,
Erica