AnjolaOluwa Badaru - Lucid
Machinelearning, SciKit, Python, Classifier, Dataset 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** ==================== ![](/storage/426/images/img-h8y59m63lw.png) ![](/storage/426/images/img-nf8ur7n67z.png) * Calling the aboce funstion and passing it the two image list * Setting the classes. 1 for Plate\_Numbers and 0 for Negative Images ![](/storage/426/images/img-5x7cfsjzlx.png) * A function for displaying images **Build Model** =============== * **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 ![](/storage/426/images/img-lboar4ksvx.png) * The function to show image prediction ![](/storage/426/images/img-gv90e5eoiq.png) * Since we don't have a test set, we predict our model on our training set. **KNN Classification** ====================== * From above score we can see that KNN performs poorly compared to Logistic Regression ![](/storage/426/images/img-efbqthpbso.png) ![](/storage/426/images/img-qv2yy4vzyz.png) **Radius Nearest Neighbor** =========================== ![](/storage/426/images/img-uy4ey5wuim.png)

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UAT 2

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