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Bosch Production Line Performance Data Science Project

In this data science project, we will predict internal failures of Bosch using thousands of measurements and tests made for each component along the assembly line.

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What will you learn

  • Understanding about Manufacturing domain and its failures
  • Working with large real-time dataset
  • Feature Engineering
  • Working on Extratree Classifier
  • Working on Ensembles methods
  • New evaluation metrics

What will you get

  • Access to recording of the complete project
  • Access to all material related to project like data files, solution files etc.


  • Language used: Python

Project Description

A good chocolate souffle is decadent, delicious, and delicate. But, it's a challenge to prepare. When you pull a disappointingly deflated dessert out of the oven, you instinctively retrace  your steps to identify at what point you went wrong. Bosch, one of the world's leading manufacturing companies, has an imperative to ensure that the recipes for the production of its advanced mechanical components are of the highest quality and safety standards. Part of doing so is closely monitoring its parts as they progress through the manufacturing processes.

Because Bosch records data at every step along its assembly lines, they have the ability to apply advanced analytics to improve these manufacturing processes. However, the intricacies of the data and complexities of the production line pose problems for current methods.

In this data science project, you will use production line dataset to predict internal failures using thousands of measurements and tests made for each component along the assembly line. This would enable Bosch to bring quality products at lower costs to the end user.



Data Scientist / Business Consultant at GE

3 years of rich working experience in BIG Data, Business Intelligence & Analytics with CMMI Level 5 Organizations in BFSI, Manufacturing Sector. Excellent written and oral communications, strong analytical and problem solving capabilities. Constantly learning and experimenting emerging open source tools and technologie see more...