How to approach any Machine Learning problem?

The process to approach any Machine Learning problem can be divided into the following steps ?

1. Study, Understand, And Analyze the Problem

Study the problem very well to understand what we have as a problem and what is our target model. Is it of type classification, clustering, regression, or reinforcement learning?

Analyze the data and define the data type and determine the data classification. Is it structured data, unstructured data, time series data, or text data? This analysis is mandatory to select the correct algorithms and evaluation metrics.

Then set the performance metric. This depends on the problem type. The performance metrics set could include precision, accuracy, recall, mean squared error, ROC-AUC, or f1-score.

2. Data Collection and Understanding

Collect all problem related data from multiple sources to be enough for insuring efficient model insights.

Explore the data to clarify the data distribution, determine the missing values and outliers and determine the relationships between the variables.

3. Preprocessing

Clean the data by removing the duplication and handling the missing values. Then determine the inconsistent data and the outliers.

Organize the data in a way to increase the model performance by scaling the existing data to standard range, categorizing the date and converting to numerical format like one-hot encoding, label encoding, etc.

Select the data features based on correlation analysis, dimensionality reduction techniques, etc.

Then split the data into training, validation and test the data sets to increase model tuning.

4. Selection of ML Model and Building

Choose the suitable algorithms according to the problem type and data features.

For example,

In the case of Classification, the used algorithms are Decision tree, Logistic regression, Gradient boosting, Random forest, SVM and Neural networks.

In the case of Regression, the used algorithms are Linear regression, Decision trees, Ridge/Lasso regression, Random forest, Gradient boosting.

In the case of Clustering, the used algorithms are Hierarchical clustering, DBSCAN, K-Learns.

In the case of Deep learning, the used algorithms are, CNNs, RNNs, Transformers.

After completing the algorithms selection process the models must be trained on the training set.

Then find the appropriate hyper parameters. It could be performed by using Random search, Grid search or Bayesian techniques.

And evaluate the model performance by checking the model over fitting, under fitting and model stability which is done by using cross validation technique.

5. Improve the selected model

The improvement of the model include errors analysis to determine the necessary model adjustments to increase model efficiency.

Through the step of model improvement, regularization techniques like L1 and L2 regularization are used to reduce the model complexity in case of overfitting.

6. Test and Validate The Model

After the selection of effective model, a model test should be performed on the determined test dataset to evaluate model performance.

The model evaluation should be performed by using multiple metrics and change the input data slightly to be certain about its effective performance.

7. Deploy the selected model

Deploy the selected model to perform the task in a production environment to make real-time predictions by using platforms like Docker or cloud services like AWS, Azure, and GCP.

Then continuously monitor the performance and try to determine any performance degradation.

8. Process Iteration

The iteration is the principal of machine learning through this process continuous model improvement could be performed by revising the steps and update the model accordingly.