The basic steps for building ML model have been described below
- Collecting and preparing your data
You need to collect all the relevant of the process for creating the ML model for that process. You can use APIs to collect data from various sources or merge existing databases and extract data from them. Once collected, the quality of the data needs to be ensured because the quality directly affects how your ML model will fare.
- Preparing quality data sets for your ML model
- Ensuring correct data format to feed to the model
- Deal with missing data by eliminating them or use estimators to input the missing value
- While dealing with categorical data, map the ordinal features and encode the class labels
- Split data into subsets
- Decompose the data into multiple parts to capture existent relationship
- Combine transactional data and attribute date
- Undertake data normalization
- Use Principal component analysis to find out the patterns existent in data by studying correlations existing between the features
- Hand over your data to a data scientist for intricate analysis of the final data quality
- Select the algorithm most suited for the ML model you are going to create
The various algorithm models and their applications in the ML projects have been mentioned below
- Logistic regression- predicting price
- Random Forest- Phising and Fraud detection
- Recuriing neural networks- Voice recognition
- Estimating the value of data using regression algorithms
- K-Means- Segregation and segmentation
- Naïve bayes- Noise filtering
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- Create an evaluation protocol
To measure our progress in building the ML model, create an evaluation protocol
- Create the validation set
For this you need to split data into two parts, one used for testing and the other for training your data model
You can also used K-fold validation method or iterative K-fold validation with shuffling
- Training your ML model
For this you need to define the name of the model based on model properties, upload the data set, let your model run the defined algorithms for processing the data, calculate the performance across pre-defined metrics, check for consistency of the run. Test whether the ML processing is able to solve the business problem. Check whether it has repetitiveness in solving problems with the set of pre-defined rules.
- Tune the ML model’s hyperparameters
Tune the ML model’s hyperparameters or find the best hyperparameters combination using Grid Search Cross validation.
The process involves
- Setting the parameter grid
- Setting the scoring method and the number of folds
- Building K-fold object with the required number of folds
- Building Grid Search object with the chosen ML model and fitting it