Machine Learning algorithms don’t work so well with processing raw data. Before we can feed such data to an ML algorithm, we must preprocess it. In other words, we must apply some transformations on it. With data preprocessing, we convert raw data into a clean data set.
There are several different types of traffic signs like speed limits, no entry, traffic signals, turn left or right, children crossing, no passing of heavy vehicles, etc. Traffic signs classification is the process of identifying which class a traffic sign belongs to. In this Python project example, we will build a deep neural network model that can classify traffic signs present in the image into different categories. With this model, we are able to read and understand traffic signs which are a very important task for all autonomous vehicles.
For this project, I am using the public dataset available at Kaggle:
The dataset contains more than 50,000 images of different traffic signs. It is further classified into 43 different classes. The dataset is quite varying, some of the classes have many images while some classes have few images. The size of the dataset is around 300 MB. The dataset has a train folder which contains images inside each class and a test folder which we will use for testing our model.
In neural networks, Convolutional neural network (ConvNets or CNNs) is one of the main categories to do images recognition, images classifications. Objects detections, recognition faces etc., are some of the areas where CNNs are widely used.
Using XGBoost, Random forest, KNN, Logistic regression, SVM, and Decision tree to solve classification problems
Assume that you are employed to help a credit card company to detect potential fraud cases so that the customers are ensured that they won’t be charged for the items they did not purchase. You are given a dataset containing the transactions between people, the information that they are fraud or not, and you are asked to differentiate between them. This is the case we are going to deal with. Our ultimate intent is to tackle this situation by building classification models to classify and distinguish fraud transactions. Why Classification? Classification is the process of predicting discrete variables (binary, Yes/no, etc.). Given the case, it will be more optimistic to deploy a classification model rather than any others. I have used the dataset from kaggle