Machine Learning Task-1
My First machine learning experience hasn't been all its promise. However I remain undeterred in my quest to come up with automated algorithms that make either forecasting, pulling data together for process actuation and machine intelligence seamlessly executed. At this task I have gotten a lot of help and direction from other interns. Got a chance to put @TopeA1 through the very first stages and giving him support where needed. It turns out he helped me hurdle through kinks in my code line up. It indeed is a worthy exercise teaching what one believes they have learned well. My abilities at:
have definitely improved.
It took me a while to get started with this task as I wanted to understand it accurately before proceeding. Eventually I realized the best way to get through with it is just START. Prior to starting, I knew I was to assemble data for classifications code sorting but remained clueless about how to write the codes.
Quickly I read through the thread of conversations on slack and called @vahiwe when I realized time was ticking. Kraggle was a good resource for datasets and I realize there is almost no learning that Medium posts would not come-in valuable to.
After saving my files into two folders and naming appropriately, I proceeded with writing my code accordingly:
Plate Number Classification
Prepare the Data
- Calling the aboce funstion and passing it the two image list
- Setting the classes. 1 for Plate_Numbers and 0 for Negative Images
- A function for displaying images
- Logistic Retrogression Model
- Here we import logitstic RegressionCV from sklearn.
- Initialize the LRC
- Fit our data into the model
- Print the model accuracy on our training set
- The function to show image prediction
- Since we don't have a test set, we predict our model on our training set.
- From above score we can see that KNN performs poorly compared to Logistic Regression
Radius Nearest Neighbor