In this post, I have shared a process to build a model using Machine Learning.
Here are three layers of ML Model
- Data
- Modeling
- Prediction
Every project you need flow. In this ML modelling, a hidden process involved.
I am explaining the steps involved in building the ML model. There is a total of three steps to developing a model.
1). Historical Data
The first thing is you need the right kind of data. The data can be your old data that acts as an input to a machine learning model.
| S.No | Name | Age | Income | Target |
| 1 | Ramu | 23 | 50,000 | Unmarried |
| 2 | Krishna | 27 | 80,000 | Married |
2). Model Building
The concept of model building is you need to assign your data to your algorithm.
Data ==> Model
3). Internal Mechanism of Algorithm
data = load_data("data/people.csv")
model = build_model(data, target="Marital status")
new_data = load_data("data/new_people.csv")
predictions = model.predict(new_data)
Model Evaluation
data = load_data(...)
training_data, testing_data = split_data(data)
model = build_model(training_data, target="Marital status")
true_values = testing_data.extract_column("Marital status")
predictions = model.predict(testing_data)
accuracy = compare_predictions(predictions, true_values)
In the model evaluation, feed only some of your data to algorithm. The remaining data you can use it for evaluation/testing.
Feed input and get answers from the model, then compare these results to actual/true values. Then you will know how your model is working.
Summary
In the testing for the given input, it should tell about a person’s marital status. The accuracy of the model will be calculated based on right or wrong answers.
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