Welcome back to my Machine Learning blog series! In the previous part, you were introduced to the fascinating world of Machine Learning and we explored its basics, importance and applications in various industries. In this part, let's dive deeper into the Machine Learning process, breaking down each step and looking at some examples along the way.
If you have missed out on the previous part where we looked at some ML fundamentals, click here
The Machine Learning Process
The Machine Learning process consists of several interconnected stages, each contributing to the development of a robust and effective model. Let's walk through each step and understand it with an example to make things clearer.
1. Data Collection and Preparation
In this initial phase, we gather the data that our model will learn from. The quality and relevance of the data directly impact the performance of the resulting model. Clean, comprehensive, and representative data is crucial for a successful Machine Learning project.
Example: Imagine you're building a model to predict housing prices based on various features like square footage, number of bedrooms, and location. You collect data from real estate listings, ensuring you have a diverse range of properties to capture different housing scenarios.
2. Data Preprocessing
Raw data is rarely in a suitable format for ML algorithms. This step involves cleaning, transforming, and organizing the data to make it ready for analysis. This might include handling missing values, encoding categorical variables, and scaling numerical features.
Example: In your housing price prediction project, you find missing values in the "number of bedrooms" feature. You decide to fill these missing values with the median bedroom count for the respective neighborhood, ensuring the data is accurate and complete.
Check out this blog which is dedicated to exploring ways of handling categorical variables for a deeper understanding: Click here
3. Feature Engineering
Feature engineering involves selecting and creating the most relevant features for your model. It's about highlighting the information that matters and discarding noise.
Example: For your housing price prediction model, you create a new feature called "price per square foot" by dividing the price by the square footage. This could help the model account for variations in property size.
Check out this blog where we took a deep dive into EDA for a better understanding of this stage: Click here
4. Model Selection
Choosing the right algorithm for your task is essential. Different algorithms are suited for different types of problems, and selecting the appropriate one can significantly impact your model's performance.
Example: You decide to use a regression algorithm, such as Linear Regression, for your housing price prediction task. Regression algorithms are well-suited for predicting numerical values like house prices.
5. Model Training
This is where the magic happens. You feed your prepared data into the chosen algorithm, allowing it to learn from the patterns in the data. The algorithm adjusts its internal parameters to minimize the difference between its predictions and the actual values.
Example: You train your Linear Regression model using your housing dataset. The algorithm learns the relationships between the input features (square footage, number of bedrooms, etc.) and the target variable (price), so it can make accurate predictions for new data.
6. Model Evaluation
After training, you need to assess how well your model performs. This involves using evaluation metrics to measure its accuracy, precision, recall, or other relevant criteria.
Example: You evaluate your housing price prediction model using metrics like Mean Squared Error (MSE) or Root Mean Squared Error (RMSE) to quantify how closely its predictions match the actual prices.
7. Model Tuning
If your model's performance isn't satisfactory, you can adjust its parameters or explore different algorithms to improve its accuracy.
Example: You experiment with different regularization strengths in your Linear Regression model to find the one that minimizes prediction errors and produces more accurate results.
8. Model Deployment
Once you're satisfied with your model's performance, it's time to deploy it to a real-world environment where it can start making predictions on new, unseen data.
Example: You integrate your trained housing price prediction model into a real estate website. Users can input property details, and the model provides estimated prices based on its learned patterns.
These are pretty much the key stages of the Machine Learning process. But, most of the time, we will have to redo some of these stages to get the desired metric values. We might have to trace back where the problem is and re-do everything from that stage.
In the next part of our series, we'll explore one of the fundamental types of Machine Learning: Supervised learning. We'll try to understand it on a deeper level and also look at different algorithms that come under it with engaging examples to enhance our understanding. So, stay curious and keep your Machine Learning journey rolling!