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Understanding the problem statement
Importing the dataset and importing libraries
Performing basic EDA and checking for null values
Handling imbalanced and Noisy dataset
Imputing the null values filling them using appropriate method
Changing categorical variables into numerical vectors
Selecting the best evaluation metrics
Applying probabilistic model BernoulliNB for training
Applying ensemble model Random Forest Classifier for training
Applying ensemble model Extra Tree Classifier for training
Applying XGBoost Classifier for training
Defining parameters for applying GRID SEARCH CV
Using Cross Folds Validation to prevent overfitting
Selecting the best model
Using Correlation and Violin plot for selecting best features for the model
Training the final model with the best features selected and making the final predictions
Saving the predictions made in the form of CSV
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.
