How to build machine learning models with Python

Machine learning is an aspect of Artificial Intelligence, which provides computers the ability to teach themselves to automatically improve and learn. It happens to be one of the greatest fields enriching industries in healthcare, finance, entertainment, and social media. Hence, a beginner’s guide to building the first model with Python is essential.

In this article, we will go through  how to build machine learning models with python.

Python and Required Libraries

First, make sure that you have Python installed on your system. Python is one of the best languages in use today. If you are new to it, there are a lot of online resources and tutorials that will take you through the process of installing the software.

Now that you have access to Python, let us set critical libraries. These libraries are critical for data manipulation analysis and machine learning algorithms.

Here are some of the basic tools:

NumPy: It is a library of useful functions for numerical computing and is the basis of many of the machine learning algorithms.

Pandas: Pandas are quite good at manipulation and analysis. It handles data structures by providing data structures like DataFrames that makes it much easier to run operations on your data.

Matplotlib: One of the most critical steps in a machine learning process is to visualize it. Matplotlib aids with different plots and graphs that shed light on characteristics one desires to learn about the data, providing pattern detection.

Scikit-learn: The powerhouse library provides machine learning algorithms in domains like classification, regression, and clustering.

Data Acquisition

Every machine learning project requires data. A model learns from these data to make predictions. The type of data that is required varies entirely based on the problem you're trying to solve.

Here are some ways to obtain data:

Public Datasets: There are several public datasets on topics as divergent as weather patterns and ratings of movies. They are a good resource for one to practice machine learning. Look at websites like the UCI Machine Learning Repository, Kaggle, and OpenML, and find those datasets that interest you the most.

Web Scraping: This becomes beneficial when one needs to do something out of a public dataset. It is the collection of data from websites. Just make sure you read over their terms and conditions before scraping data from a website.

Tools for data collection: Sometimes, you have to collect the data yourself. Depending on the kind of data that is surveyed, a couple of tools and platforms exist that are helpful in collecting data. For example, online questionnaires are used to collect user preferences or mobile apps developed to collect sensor data. 

Data Preprocessing

The raw data is a perfect format for machine learning. But data preprocessing is one essential step that focuses on the cleaning and the preparation of your data.

Here are common data preprocessing tasks:

Missing Value Handling: Most real-world datasets contain missing data points. Depending on your data, you would either have to just remove rows that contain missing values or fill them up with summary statistics like mean or median imputation.

Encoding Categorical Variables: Most machine learning algorithms work at best with numeric data. Now, in a scenario where your dataset is made of these categorical variables, such as text labels, you will be required to encode them into numerical values. Some standard encoding techniques include one-hot and label encoding.

Scaling Numerical Features: These are features in one's dataset that have different ranges. For instance, in a dataset including the features of "age" and "income," here the scale of "age" would be between 18 and 65, very different from "income," which might range between $20,000 and $200,000. Scaling features puts all these features on the same footing like the model is being trained. Standard scaling techniques include standardization and min-max scaling.

Cleaning and features engineering will give you the biggest help which is the most important and accurate machine learning model.

Exploratory Data Analysis (EDA)

EDA is the most overlooked area in machine learning and help you get familiar with data. The business objective of this exploratory data analysis is to review statistically and through visualization characteristics in the dataset for any patterns, trends, or problems indicative of potential biases or noise. This would be a good point at which to examine summary statistics, create histograms, plot scatter plots. 

Model selection and training:

Now that you have explored and understood your data, it is time to select the appropriate machine learning model. The model to be chosen would depend upon the type of problem you are trying to solve.

These are some of the common problem along with their corresponding model categories:

Classify: If you need to predict a category or class label, such as spam versus non- spam emails, then you would want to use a classification model, and Scikit-learn has options from basic ones like support vector machines and random forests through those a bit more complex like K-nearest neighbors.

Regression: If you are solving a problem where you need to explain a continuous value for example, house or stock price then it’s best to use a regression model. First, this would be linear regression or either a decision tree or polynomial regression will work.

Feel free to run other models on your data. Scikit-learn has a consistent interface for running many algorithms.

Now that you've selected a model, we have to break our data down into two fundamental sets: Training and test data. If you will, think of your data as a deck of a thousand flashcards. Training Data is that deck of flashcards that will be used to build learning models with python. Your model will learn this dataset for patterns and relationships. 

Testing Data is much like another deck you had set aside just for quizzing. Once trained, this model gets tested to show how it generalizes to unseen data.

Model training is the process of passing the training data through the model, which learns fundamentally the underlying patterns or relationships in the data. It may, therefore, predict new, unseen data points.

These metrics will inform you how good or how bad your model is performing. 

In case the results are not satisfactory, then there are majorly two significant ways to improve your model:

Hyperparameter tuning: Most machine learning models have hyperparameters that control their behavior. All of these parameters can be tuned to a huge extent so that your model is optimized in making a prediction. Scikit-learn provides many tools for hyperparameter tuning, but often it remains iterative and requires experimentation.

Trying a Different Model: In the case where no significant improvement was noted with hyperparameter tuning, try another different machine learning model.

Prediction:

Now that you think you have a good model at hand, it is time for making some predictions. You can train your model on the data set and make some forecasts on new unseen data points.

Perform the following steps:

Prepare New Data: The new data on which you want to make predictions should also be formatted similarly to your training dataset. Much like your training dataset, it means containing similar features like columns and data types.

Feed the Data to the Model: Now that it's prepared, feed this new data points into your trained model. Most models in scikit- learn have a prediction method that takes in this new data and returns the predicted values. 

Interpreting the Predictions: What the model gives out at the other end is problem-dependent. In a classification problem, it would predict the class label, such as 'spam' or 'not spam,' for the new data point. It also gives model probabilities for every class, showing how confident it is about its prediction.

For regression problems, it predicts a continuous value for the new data point, much like the fluctuating house price.

Conclusion

Machine learning empowers machines to learn from their experience and improvise on their performance and this has transformed many industries.

The article considers how to build machine learning models with Python, breaking them down into the following actionable steps: setting up Python and libraries, acquiring data, preprocessing data, performing exploratory data analysis, selection and training of a model, model evaluation, and making predictions.

Keep in mind that building machine learning models with python is actually a continuous process of learning. Try exploration and experimentation, and keep on practicing!

FAQs

1. What are some popular machine learning libraries in Python?

Several powerful libraries form the foundation of how to build machine learning models with Python. These include NumPy for numerical computing, Pandas for data manipulation, Matplotlib for visualization, and Scikit-learn for a comprehensive suite of machine learning algorithms.

2. How do we  find data for a machine learning project?

There are multiple approaches to data acquisition. Public datasets on various topics are available on platforms like UCI Machine Learning Repository, Kaggle, and OpenML. Web scraping can extract data from websites, but be mindful of terms and conditions. In some cases, you might collect your data using tools or surveys.

3. What is the importance of data preprocessing?

Raw data often requires cleaning and preparation before feeding it into a machine learning model. Data preprocessing involves handling missing values, encoding categorical variables, and scaling numerical features. This step ensures the data is in a suitable format for the model to learn effectively.

4. How do we choose the suitable machine learning model for a problem?

The selection depends on the type of problem you're trying to solve. Classification models are suitable for predicting categories , while regression models excel at predicting continuous values . Explore different models within these categories using Scikit-learn to find the best fit for your data.

5. How do we evaluate the performance of the machine learning model?

Once you've trained your model, it's crucial to assess its performance on unseen data. Scikit-learn offers various metrics depending on the problem type. Standard metrics include accuracy, precision, F1-score, and mean squared error or R-squared . Analyze these metrics to identify areas for improvement through hyperparameter tuning or trying a different model altogether.