A sensor is attached to the vehicle's roof while it's in motion; this allows it to capture images of the road, which might be used in an autonomous driving system. The roadways shown in these images may not necessarily be exactly flat, have sharply defined borders, or adhere to any established patterns. Methods such as edge detection, Hough transformation space, and vanishing point detection might be used for autonomous vehicle route recognition. So that it can recognise roads in the freshly processed picture from the vehicle, we normally train our model with hundreds of photographs of various roads. This

Clustering is a crucial step in descriptive statistics and data mining. It is used in many different fields of work, including data categorization and image processing, and has been the subject of much study by many different academics. We present I-BIRCH, an improved balanced iterative reducing and clustering technique that makes use of hierarchies. It works well with massive datasets and is an unsupervised data mining technique. The algorithm begins by clustering data points with a single dimension, and then it moves on to cluster data points with many dimensions in order to get the optimal clustering with a single view of the data. The "noise" (data points that do not form part of the underlying pattern) is something it can manage. Clustering calculations take O(n2) time and use a distance matrix that is O(n2) huge. When mining complicated or massive datasets, this kind of grouping is a necessary component. When there is information about the heart, such an ID, a nam

One of the most popular and widely utilised platforms for digital marketing, social media allows firms to keep tabs on public trends and preferences, and it also provides valuable insights into consumer behaviour. The number of false social media accounts that disseminate misinformation is on the rise. In order to address the issues surrounding the identification of false social media profiles, this study examines several machine learning techniques.Jupyter Notebook makes use of Python and a number of machine learning and data analytics libraries, including Numpy, Pandas, Sklearn, and others. Using AUC Score, Confusion Matrix, and total number of Fake and Genuine Users discovered, this article compares three machine learning algorithms: Support Vector Machines (SVM), Random Forest, and Neural Networks. For easier examination and comparison across all methods, results are shown as graphs. The dataset that was used for this project can be found in the following link: At t