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Understanding Machine Learning Projects Pipeline

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Machine learning is now being used all around the world and it’s helping the analytics team greatly in saving costs and improving business decisions.

A Machine learning project starts with Raw data and Ends with a web application that can predict outcomes and generate insights from raw data.

The following Steps are Involved in the Machine Learning projects pipeline.

Step 1: EDA

EDA stands for Exploratory Data Analysis where we explore the raw data, determine the target variables, analyze the data to look for missing values, analyze the distribution of variables and features, datatypes, outliers, data size, correlation of input features with the target column, relationships among the features and look for any visible patterns in the data.

Step 2: Data Transformation and Cleaning

Every machine learning algorithm requires data to be in a specific format for it to perform at its best. For example, Linear Regression, Logistic Regression, PCA, and some clustering algorithms. These things use distance to calculate the similarity between data points and require the features to be on the same scale, as having them on different scales may lead our algorithm to give more importance to features, having more magnitude, and less importance to features having less magnitude. In such cases, we use a data normalizer to normalize our data. Apart from that treating missing values, treating outliers, and encoding categorical features is also important for the optimal performance of algorithms.

Step 3: Feature Engineering

Feature engineering is simply the science (and art) of extracting more information from existing data. Here we try to generate features by using existing data. For example, we can use a timestamp column in our dataset to generate month, quarter, and year columns. In addition to adding these to your feed, our algorithm with additional information of data can significantly improve algorithm performance. We can transform existing columns to make them more useful for an algorithm.

Step 4: Train Test Split

An important step in our machine-learning pipeline is to test how well our model is performing on unseen data. so we keep some data unseen to our model from the given data and use it later to evaluate model performance on unseen data, this process of dividing the given data into train and test data is known as train test split.

Step 5: Selecting Model

Model selection is an important step and there are various ways to do it. Ideally, we compare the evaluation results of different models and select the model which performs best. But there can be other criteria too for selecting the best model. For example, in some cases, KNN can give the best results but it is very slow in performance as it compares new data points with each and every data in our given training dataset to predict the result which makes it very slow, so it can’t be used in most of the production scenarios as most of the time users don’t like to wait too long for the results. When selecting a model look for the overall purpose, accuracy, and speed of the model. You can start with the model of your choice and keep comparing various model results to select the best model.

Step 6: Model Training and Hyperparameter Tuning

Once you have selected the model, train it using the training dataset that we created in the train test split process and tune the hyperparameters to select the best hyperparameters for your specific use case. You can use a random search CV or grid search CV method to tune your hyperparameters.

Step 7: Evaluate Model

Once you have trained your model it’s time to evaluate it on the unseen data. Take the test data from the train test split and evaluate the model on the predictions given by your model using evaluation metrics like Accuracy, Precision, Recall, F-score, or AUC for the classification model and mean absolute error. Root mean squared error or R-square for regression models. Select the suitable evaluation metric as per the requirement,

Step 8: Deploy Model.

Once you’ve got your best model it’s now time to deploy it so that the end users can use it by sending API requests. You can create an Endpoint using Flask or FastAPI and use your model in it to predict the results of the input data from API calls. You can deploy your flask app on Cloud or local servers as per the need.

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Akshay Dharmik

I work at Perficient as a Senior Technical Consultant and have a firm understanding of technologies like Python, Pyspark, AWS, and SQL. I am passionate about exploring new technologies to keep myself up to date with the latest trends.

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