Model building in machine learning using 8 steps

In this article, we will study model building in machine learning using 8 simple steps. It is necessary to have basic knowledge and understanding to build a predictive model in python. Python is a more powerful tool especially for data scientists, machine learning engineers, and AI experts. So building machine learning models in python makes it easy to solve any challenges in a few steps.

Step 1: Input data:

Input data play a vital role in model building in machine learning.  Data is necessary to feed any model and on thr basis of that data it predicts, what will be the output.  To build a model in machine normally data is occurring in the form of series data, image data, and video data. Input data quantity and quality have a great impact on the final prediction result.

Step 2: Data preprocessed:

Almost all the input data is in the raw form, so we can say in unstructured form or unstructured data.  Model building in a machine have need good quality and structured data. In the data preprocessing method we used a different technique to handle data quality, unbalanced data, filling missing values, and enhancing the quantity of data.

Step 3:Normalization and feature selection

In this step, the data is normalized by using the normalization method. To build an ml model, it is also a necessary step to select important features to reduce the training time and get better output performance. In feature selection, we can use RFE, Brouta, or Knn algorithm to get the most important feature, with the perspective of our input data.

 Step 4: Training and testing set:

The structure data is divided into traing set and testing set. The standard ratio of this division is 80% in the training set and 20% in the testing set if the data quality is good otherwise the division will be 70% and 30% in the training and testing set respectively.

Step 5 Train the building model in Machine learning:

Here, we used training data to train already existing popular machine learning model namely linear regression k –nearest neighbor, (KNN), Random Forest (RF), support vector machine (SVM),  Gradient boosting machine  (GBM), Extreme gradient boosting machine (XGBM), Multilayer perceptron (MLP)  and many more. These models are extensively used to solve world problems and challenges. Every machine learning model’s performance depends on data, we can,t say the specific model is more efficient for any dataset. This model is well trained on the dataset and accuracy can be enhanced by changing hyper-parameter by a hit and trial method.

Step 6: predict the building model in Machine learning:

Test data set is used to predict the output of the model and it is make sure that the test data set have no single data similar to the training dataset. In this section, you can find the confusion matrix, the area under the curved( Auc), and the receiver operating characteristic curve (ROC).

Step 7: Save the building model in Machine learning:

The building model is saved by using joblib or Pickle method in the form of a .pkl file for further use. When once your model is saved, you just call it anywhere it gives output prediction as you have trained. You can deploy this model on the server using Django or Flask.

Step 8: Deploy the building model in Machine learning:

This is the final step, we deploy the model with the help of Flask and putty. The putty is used to access the server and we deployed it on the server and from the client-side, we can get results according to our trained model.

Conclusion:

In conclusion, the step-by-step machine learning model deployment is discussed, it starts from input raw data to develop a useful model and furthermore deploys on the server using flask. In practice, we will do in upcoming content and try to cover the whole step by step in a practical way. In this article we got raw data, performing some pre-processing to become representable then performing feature extraction and feature selection, dividing the data into training and testing, next step is to train the model, perform prediction and deploy on the server. I hope you will enjoy this reading.

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