Whether you’re a novice or a seasoned data scientist in the data science industry, you will find some awful practices which have been often overlooked.
At times, these practices could take a data scientist’s career for a toss.
Failure is a detour; not a dead-end street. However, the idea here is to help you identify those mistakes and how you can avoid them.
Let’s get back to the mistakes a data scientist may often fail to address. Below is the following list you need to keep in mind while taking up any data science projects.
Point#1: Focus on using the relevant dataset
Most often, a data science professional tends to use the entire dataset while working on a data science project. Ensure you do not make this mistake. The entire dataset might have several implications like missing values, redundant features, and outliers. You wouldn’t want to get caught breaking your head trying to figure out what’s important and what’s not, right?
However, if the dataset contains a tiny fraction of imperfection then simply eliminating the imperfect data from the dataset would do the trick.
But if it is large and significant then perhaps you might need to use other techniques to solve or approximate the missing data.
However, before making any conclusions and deciding which machine learning algorithms to use, the data science aspirant needs to identify the relevant feature present in the training dataset. The complete process of transforming the dataset is known as dimensionality reduction. Such a feature is significant due to these three reasons –
- Simple to use: It is not complex and easy to interpret, even when features are correlated with one another.
- Efficient computation: Any model trained at a lower-dimensional dataset will always be computationally efficient i.e. the execution of an algorithm may take lesser computational time than usual.
- Prevents overfitting: Overfitting takes place when the model tends to capture real and random effects, but the dimensional reduction or feature selection avoids such incidences to happen.
Dimensional reduction or feature selection techniques are some of the ideal methods to eliminate the unwanted correlation between features, thus, boost quality along with the predictive power of the machine learning model used.
Point#2: Comparison of different algorithms
It’s obvious for you to make sure you make the right choice by picking the right model. This can be achieved provided you’ve made a proper comparison between different algorithms.
Let’s take an example, imagine you’re building a classification model and you’re yet to finalize which model to pick. What would you do if you still cannot figure out which algorithm to pick?
Therefore, a comparison between different algorithms is important while building any model.
For the classification model, you can compare the below algorithms –
- Decision tree classifier
- Logistic Regression classifier
- Naive Bayes classifier
- K-nearest neighbor classifier
- Support Vector Machines (SVM)
And for the linear regression model, you might need to compare these algorithms –
- Linear regression
- Support Vector Regression (SVR)
- K-neighbors regression (KNR)
Point#3: Scaling of data prior model building
To make sure you have the right modeling techniques you need to first scale the data, this will not only boost the power of prediction but quality too.
For instance, if a data science professional plans to build a model that can predict creditworthiness based on predictor variables like credit scores and income. Wherein the income could range anywhere between USD 25,000 to USD 500,000 and credit score ranging from 0-850. So, if you cannot scale these features, the model you’re building can be biased toward the credit score or income feature.
To normalize such features and bring them at a constant scale, you will need to decide on utilizing standardization or normalization features.
Point#4: Quantifying random error or uncertainties in your model
For every machine learning model, you will always find the presence of a random error. Now, this random error could have been from the nature of the dataset, from a random target column, or perhaps from the testing set during model training.
You always need to remember quantifying random error effects. Only after which you would see improvements in the quality and reliability of your model.
Point#5: Make sure to tune hyperparameters in your model
Low-quality and non-optimal models are due to wrong hyperparameter values that have been used in the model.
It is critical to train your model against all types of hyperparameters for the best results. The predictive power of a model depends on hyperparameters, which is why it is important to tune hyperparameters. A default hyperparameter will not always result in an optimal model.
Point#6: Never assume that your dataset is perfect
Data is the core key to everything, from doing a machine learning task to data science knowledge.
You will find data to be in multiple forms – structured and unstructured. They can be presented to you in the form of a text message, an image, video, voice data, and perhaps numerical data. But the prediction of a model is solely based upon the quality of data used. One of the major reasons why it is crucial to crosscheck the source of the data and its reliability. Data that may seem perfect might contain multiple errors.
Certain factors that can eliminate the quality of the data includes –
- Unbalanced Data
- Size of Data
- Wrong Data
- Outliers in Data
- Missing Data
- Lack of Variability in Data
- Redundancy in Data
- Dynamic Data
Though mistakes help you grow, try not to make the same mistake twice.