Machine Learning Projects are what sets you apart from newbie ML enthusiasts.
More and more students are enrolling in machine learning courses and finding solutions online.
However, cracking machine learning is not limited to enrolling for classes, taking notes, appearing for tests and getting a certification. It is crucial to put all you’ve learned into practice, and we mean real-world practice!
You see the human mind is nothing like a machine. We don’t have fancy algorithms which learn once and never forget! We need to brush up and perfect whatever we’ve learned through continuous practice.
How do we do that?
We ace machine learning projects!
Why should you care about Machine Learning projects?
- You get to test the ML algorithms you learned all about in theory and controlled environments!
- You develop a deeper understanding of data. As you explore your dataset, you are able to better identify with the problem presented to you in the first place.
- Estimate relationships and patterns in your data before you deep dive.
- Assess which algorithm is better suited where and understand the underlying reason.
Now that you know why you absolutely must fine-tune your skills using machine learning projects, let’s look closely at what it takes for the ride!
Where do I get my dataset?
As an amateur, it can be confusing to start working on a dataset. In the spirit of starting small, we’d recommend you find a light and less complex dataset, to begin with.
These are some platforms from where you can source your data:
All things Google are the best, aren’t they? Google’s online platform, Kaggle, for data scientists and machine learning buffs. The vast online community is supportive and talented, learning as they communicate. You can find a variety of data-sets on Kaggle for your project and also abundant help with the same!
- UCI Repository
UCI Repository collects databases, data generators and domain theories. The repository currently has around 474 data-sets to work on and has been trusted by machine learning students to explore data.
Data.world is another open data source for budding Data Scientists. You can use the platform to source, copy, modify, analyze and download data to work on your own machine learning projects.
These are a few sources where you can look for your desired dataset. Remember there is no best dataset or machine learning algorithm, you need to practice on a wide range and you can get started here!
How to approach Machine Learning projects?
Now that the datasets are ready, let’s talk about how you should tackle them. For any given project, we have identified 7 steps that you must follow, like an outline, to ace machine learning projects. Let’s find out what these steps are!
Step 1: Data Reading
The first thing you would want to do is read the dataset using a Python data frame. Understanding how your data looks like, you’re better equipped to decide how to go about it.
Always know your objectives when exploring the data.
For instance, if your objective is predicting stock prices, you’d be more concerned with numerical data and if the goal is classification, you’d be more concerned with text data.
Based on your objectives, you will need to view your datasets under the purview of:
Numerical Attributes: Your focus would be on numerical variables especially in cases where you have to predict numerical values such as sales prediction, price predictions and so on.
Character Attributes: Extensively used in classification problems. You need to focus on character variables when labeling output as male/female or yes/no.
The next thing to do is to download the required Python package. For example, using Numpy and Pandas for reading a csv. file on Python data frame is a popular choice.
For instance, you can download your dataset from the different repositories as a csv. file and then import it to your Jupyter notebook. As an INSAID student enrolled in a machine learning course, you could cut down on downloading and access the dataset using raw Github links directly.
Once you have your hands on the dataset, data reading would help you understand how the data is structured, right from the number of rows and columns to data dimensions and basic descriptive statistics of your data.
Step 2: Pre-Processing
Now that you have a broad understanding of the data and consequently the variables you’re dealing with, the next step is pre-processing.
Here, you need to identify what variables need to be transformed or modified before you can begin your analysis. There can always be discrepancies in your data in the form of missing values, outliers and so on. You can eliminate such inconsistencies by cleaning your dataset based on your initial observation.
While our goal is to make the dataset concise and crisp, not all outliers always need to be eliminated.
Step 3: Data Normalization
Data normalization also requires adjusting and organizing your data to make it ready for further analysis. You need to understand that data normalization is important so that your model is saved the effort of dealing with inconsistencies in datasets.
You might come across data values that appear as clear anomalies. For instance, the age of a person cannot be in 180 or the number of days in a year can’t be negative and so on.
Another reason for data normalization is different units representing different factors or columns in datasets. You cannot compare the height of different people in feet with their weights in kilograms. You need to figure out a way to adjust the values for their units, such that they can be made unitless for comparison.
You need to understand that normalization does not change the dataset completely. You need to make normalize data to remove redundant features without while retaining all information. This way you keep the meaning of the data intact while only changing its representation.
Step 4: Data Standardization
Data standardization is imperative for ensuring a consistent dataset. Like we discussed in normalization, comparing values in different units would be useless as they do not share a common scale.
It’s very literally comparing apples to oranges!
Standardizing data uses z-testing. Let’s explain with an example. Consider you and elder your sister appeared for a math test in your school. She was asked to solve 30 questions in one hour and scored correctly on 25. You, being younger, were given 20 questions to solve in an hour and managed to answer 13 correctly.
When it comes to figuring out who performed better, the denominators are different! Like we studied in school, to compare these two scores, we’ll convert them into percentages. That way while your sister bagged 83.33%, you scored 65%. Now, we can safely say that your sister outperformed you.
Just as percentages make comparisons easier by bringing them to a common denominator, z-tests used in standardization also converts values to z-scores which aren’t dependent on individual units, and hence useful in comparisons.
Step 5: Data Split
The next step deals with slicing your data. To aptly gauge the feasibility of your machine learning model, you need to subdivide your data into the following:
- Training Data: This is the part of data fed to your model to train it. The model observes and learns from the input and output data.
- Validation Data: Validation data is the slice of data you use to validate your model. Most projects omit the validation step but if you need a more robust and thorough result, you must use a part of your dataset to ratify and solidify.
- Test Data: This is the part of the dataset that actually generates output based on what it has learned from the training data. Here, you get an unbiased result of how effective your model is.
We’d recommend you divide your data in a ratio of 7:1:2 to most optimized results but that is a generic recommendation and the actual slices may differ with your approach, data and objectives.
Don’t get apprehensive if your machine learning course suggested different proportions to split your data into. Most people take different approaches based on their current dataset and whatever machine learning projects and cases they have worked on in the past.
Step 6: Apply the Machine Learning algorithm
After ensuring all the previous steps have been successfully completed, we finally arrive at applying the required machine learning algorithm.
Choosing the right algorithm depends on your objective, attributes of the data and required output. You can read more about popular machine learning algorithms here.
For instance, some machine learning projects require estimating house prices or predicting sales for an upcoming quarter would require you to use regression algorithms. If you’re tasked with classifying emails as spam or regular, classifications algorithms are the way to go.
By now, we hope you understand the difference between supervised and unsupervised machine learning algorithms. If not, you might want to check it out here.
Step 7: Evaluate the significance of the model
Now that the hurly-burly is done, you need to evaluate the performance of your model to understand if your results were the best that could be extracted from the data.
Depending on whether you had a regression or classification problem, you can perform a number of evaluations. Let’s see some of them.
|Mean Absolute Error||Accuracy||Pseudo F1β score|
|Mean Squared Error||Precision|
|Root Mean Squared Error||Recall|
It’s imperative to understand that the evaluation of models has its own purpose. You need to know if the model is the best choice to analyze future data and make predictions.
A rookie mistake people often commit in their machine learning projects is to overlook what their models are actually conveying. Incorrect models beget incorrect predictions or classifications. Let’s talk about two situations here:
- Overfit: Overfitting in machine learning models happens when the model is a tad too well trained for its own good. The model loses its ability to generalize. The model picks up the training data along with its noise, fluctuations and models on new, fresh datasets.
- Underfit: Underfit is also not ideal. Underfit models do not learn properly from the training data and consequently cannot apply their learning elsewhere. Underfit models fail to capture the relationship between the input and the output and you might need to restart with a different algorithm altogether.
Another solution to evaluating the model’s performance is cross-validation. Here, instead of dividing your dataset into training, validation and test data, you train the model on an entire dataset and test its performance on another, fresh dataset.
These are the seven steps involved in working on different machine learning projects. Whatever is your dataset or machine learning course, following these seven steps is a fail-safe way of extracting insights from your dataset.
Now that you’ve been coached in the workflow, you are ready to take on machine learning problems. If you have any doubts, do write to us in the comments section.