Supervised ML - Aamir P

Hey all!

In this article, we will discuss Supervised ML. Supervised learning involves training a model using labelled data, where the input data (bananas) is accompanied by the corresponding output labels. The model learns from this labelled data to make predictions on new, unseen data. So, we require previous data and previous results as input. Predicting the results become more accurate in this way.

The above picture is to distinguish fruits. The data has to be labelled or classified for different fruits in the collection. The data is divided into classes in supervised learning. So, the model is learning the features from the labelled data (i.e.) yellow colour, curved shape, etc. and learns it as per the annotation banana. In future, it can predict based on these features and give a result banana. The different methods in Supervised ML are classification, regression, naïve Bayes theorem, SVM, KNN, decision tree, etc.

We will now learn with a real-time example to understand Supervised Learning better.

Imagine you are a Data Scientist working in an e-commerce company. Your goal is to build a model that can predict whether a customer will make a purchase based on certain features such as browsing behaviour, demographics, and past purchase history. This is where supervised machine learning comes into play.

Let us learn it through the steps of the process to understand it better.

1. Data Collection

You must have gathered a dataset that has customer information like age, gender, browsing duration, etc. This is your training data.

2. Data Preprocessing

Here we preprocess the data by handling missing values, splitting and training, etc. Henceforth, to be simple we will train and test the model to check performance.

3. Model Training

We train the model using the labelled training data. For that, we can go with approaches like logistic regression, decision trees, support vector machines, etc. The model learns to find patterns and relationships between the input features and the corresponding labels.

4. Model Evaluation

Once the training is over we will be testing it. But to know the model is working fine we check the metrics like accuracy, precision, recall, etc. to evaluate our model.

5. Prediction

With a trained and evaluated model, you can now make predictions on new, unseen data. For instance, when a new customer visits your e-commerce website, you can feed their features into the model, and it will predict the likelihood of them making a purchase.

6. Iteration and Improvement

The model doesn't stop there. You can continue refining the model by experimenting with different algorithms. The iteration improves accuracy and prediction.

By utilizing supervised machine learning, you can build a predictive model that helps the e-commerce company identify potential customers who are more likely to make a purchase. This, in turn, enables targeted marketing strategies, personalized recommendations, and enhanced customer experiences.

So, that's it for the day! Thanks for your time in reading my article. Tell me your feedback or views in the comments section.

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