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Category: Data Analytics

Predictive Collision Avoidance in Connected Car Industry

Predictive Collision Avoidance in Connected Car Industry

Predictive Collision Avoidance in Connected Cars Technology proposes drivers many features in ADAS (Advanced Driver Assistance Systems) and AD (Autonomous Driving AKA Driverless car) developments.  Among them, perhaps, predictive collision avoidance system is most cherished. With progressive sensors, big and fast data, and car-to-car connectivity, predictive analytics technology solutions will one day come to zero auto accidents. Before proceeding to read further, make sure you have read introduction and details of predictive analytics in connected car industry in this post….

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Predictive Analytics in Connected Car Industry

Predictive Analytics in Connected Car Industry

Introduction to Predictive Analytics in Connected Car Industry Imagine the flawless automobile vehicle ever, like autonomous car. It exercises itself on the street even when you are having a nap, halts itself at your favorite hotel for your favorite cuisine, and awakens you just in time for a rapid touch-up before you step out of the car. If this is the matter from a science fiction film, it is coming to real life one-step at a time. Connected car industry…

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Automotive Industry Analytics

Automotive Industry Analytics

Introduction to Automotive Industry AnalyticsNowadays, vehicles can produce and collect huge amount of raw data for automated analysis. Cars can have minimum 50 sensors, maybe more than that, specially intended to gather complete facts like speed, driving performance, emissions, distance, resource usage and fuel consumption. When merged with a classy predictive analytics resolution, data scientists and analysts can convert raw unfiltered data into meaningful information. This information can they be used in decision making processes in public and private sectors….

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Missing Features: A challenge in data preparation

Missing Features: A challenge in data preparation

What is Missing Features Problem? The principle of machine learning and AI based application is “Garbage in-garbage out”. Hence, data cleaning and Exploratory Data Analysis are the key steps of data preparation phase. In the real world, the data comes with noise. The noise may include many things such as incorrect data, unnecessary extra data, missing values, missing attributes/features. Among st them, it is difficult to handle missing features problem. Missing features is one of the most common problems that…

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Incremental Clustering with example: BIRCH Algorithm

Incremental Clustering with example: BIRCH Algorithm

BIRCH (incremental) clustering algorithm : Introduction In one of the previous posts, we talked about incremental clustering with kmeans and saw an example. Here, we will see one more advanced incremental clustering technique called as BIRCH. I recommend you to read fundamentals of machine learning and information of incremental learning first before proceeding to this article. BIRCH stands for balanced iterative reducing and clustering using hierarchies. It is an unsupervised data mining system used to perform hierarchical clustering over big…

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