A Beginner’s Guide to Developing Machine Learning Models: The Full Process Explained

Machine learning (ML) is a technique that uses algorithms to learn from data and make predictions. In this post, we’ll walk through how to build an end-to-end ML system for a product recommendation engine.

Introduction:

Imagine you’re building a recommender system to suggest products to people that they may like based on their previous purchases.

There are few trends that you might see but a few that you might not. So, you want to use machine learning algorithms to identify that trends. Recommendation engines are a great use case for ML. By analyzing customer behavior data, ML models can identify patterns and suggest new products that customers might like. This increases sales and improves customer satisfaction.

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While powerful, developing a production-ready ML recommender system requires expertise across data engineering, ML modeling, and system engineering. This guide will outline the key steps involved:

But how do you build it exactly? Here is a complete step-by-step approach to do that.

Step 1: Data Collection

Collect as much historical data as possible. Collect as many features/attributes you can.

For example,

• Customer data: Unique ID, demographics, location
• Product data: Product IDs, categories, descriptions, images
• Interaction data: Customer product views, purchases, ratings, cart adds
• Transaction data: Purchase timestamps, payment mode

The more varied data we collect, the better our models can learn customer preferences.

Step 2: Data Preprocessing

Let’s say our raw customer data contains fields like name, email, age, and address. Before we can use this for recommendations, we need to clean it up.

Why?

1. Names can be formatted differently, like “John Smith” and “Smith, John”. We need to standardize this to match names that refer to the same customer.
2. Emails can have typos — betty@gmil.com instead of betty@gmail.com. We need to fix these.
3. Ages can have errors like string values “forty-five” instead of 45. We need to convert these to integers.
4. Addresses can be incomplete, missing pincodes. We need to fill or discard these.

If we don’t preprocess, we may think John Smith, John Smyth and J. Smith are 3 different customers! Or we may try to calculate average ages wrong.

For product data, we need to clean descriptions, fix images, and remove invalid product IDs. Proper preprocessing ensures our data is consistent and accurate for the next steps.

Basically, real-world data is messy. Preprocessing cleans up errors and inconsistencies so algorithms can accurately find patterns.

Step 3: Feature Engineering

Feature engineering often includes creating new features from raw data to identify better factors that contribute more towards the output variable.

With examples, imagine creating features like below,

• Customer attributes like income segment, frequent purchase days
• Product attributes like color, brand, style by analyzing data
• Aggregated metrics like total past purchases by category

This could highly influence customer’s decision to buy a product. By including these extra features, we could predict what type and color of product we can recommend to a customer. And that could be recommended on a specific day by observing the pattern through frequent purchase days.

With raw data alone, the recommendation model may struggle to uncover meaningful patterns. But the engineered features provide much clearer signals to predict each customer’s tastes and purchase behavior. This enables the model to suggest products that appeal to the customer’s preferences. Just like adding the right herbs and spices can significantly enhance the flavor of a dish!
Domain expertise helps create more informative features. Feature selection also ensures models are not overwhelmed by too many noisy inputs.

After Feature Engineering, split the data into train, validation and test data.

Note:
The validation set helps tune model hyperparameters and evaluate accuracy before final testing.
If you want to know more about test, train and validation sets, let me know by commenting!

Step 6: Model Selection

Now to decide which Machine Learning model will be appropriate for our use case, we. will have to consider a lot of factors.
For example: Type of output variable( Numerical or Categorical), how many features are you feeding into your model, how much data you have to build your model, which model is giving you good enough accuracy etc.

For example, for a recommender, common choices are:

• Collaborative filtering: Analyzes customer similarity
• Content-based: Analyzes product similarity
• Hybrid: Combines collaborative and content-based filtering

Essentially, factor in the properties of engineered features, data size and problem complexity when narrowing down the model choices. Leverage validation set performance rather than just gut feeling.

Step 7: Model Evaluation

Use the model that you’ve built on your test data to check for accuracy, precision and recall metrics (these metrics are used to evaluate its performance).

Common evaluation metrics are:

• Precision and recall for classification tasks
• Root mean squared error for regression
• Ranking metrics like mean average precision for recommendations

Step 8: Model Deployment

Once we have trained and tested a recommender model to ensure it provides accurate product recommendations, the next step is deployment. This means integrating the model into the production environment of our e-commerce website/app.
A/B testing of different model versions is useful for catching any post-deployment issues and further optimizations.

Step 9: Result Interpretation

Analyzing live customer usage data provides insights into real-world model performance. We need to track metrics like:

• Clicks and conversions for recommended products vs others.
• Monitor technical metrics like API response time, errors etc. to ensure a smooth experience.
• Run A/B tests varying aspects like recommendation algorithms, number of products displayed etc. to find what works best.

Conclusion

n summary, building a machine learning system like a product recommender involves many steps. It’s not magic — real people have to work hard behind the scenes!

1. First, we have to gather and clean up messy data.
2. Then they have to analyze the data to bring out useful patterns.
3. After that, we test and fine-tune special math recipes known as machine learning models to get the best results.
4. The final model is hooked up to websites and apps so it can automatically suggest products to customers.

But the work doesn’t end there — engineers keep tweaking and improving the system based on how real customers respond to the recommendations.

So while machine learning sounds highly technical, at its core it involves smart people combining data, algorithms and real-world observation to build helpful applications.

With the right expertise and careful systematic effort, ML can enhance many products and services we use every day — whether it’s recommending what to buy, forecasting traffic or detecting fraud. The possibilities are endless!