6 Common Mistakes to Avoid in Data Science Code

As we know Data Science is a powerful field that extracts meaningful insights from vast data. It is our job to discover hidden secrets from the available data. Well, that is what data science is. In this world, we use computers to solve problems and bring out hidden insights. When we enter into such a big journey, there are certain things we should watch out for. Those who like playing with data know the tricky part of understanding the data and the possibility of making mistakes during the data processing.

How can I avoid mistakes in my Data Science Code?

How can I write my Data Science code more efficiently?

To answer all your questions, In this article, you get to know Six common mistakes to avoid in data science code in detail.

Common MIstakes in Data Science

Ignoring Data Cleaning

In data science, data cleaning means making the data look tidy. Processing with cleaned data provides accurate results and avoids underfitting. Ignoring data cleaning makes our results unreliable and leads us wrong. It also makes our analysis confusing. We get data from various sources like web scraping, third parties, surveys, etc. The collected data comes in all shapes and sizes. Data cleaning is the process of finding mistakes and fixing missing parts.

Causes of Ignoring Data Cleaning

Example: Sales Data with Duplicate Entries -Let’s consider the analysis of sales data of a product. We may notice duplicate entries of some data due to technical issues during data collection. Ignoring data cleaning makes us proceed with sales analysis with duplicates. Consequently, the sales analysis shows high numbers, making the product seem more popular than it is.

Key Aspects of Data cleaning

This step involves careful analysis of our data for mistakes like inaccuracies and typing errors. It is like proofreading a document to ensure the information is correct.

1. Handling Missing values: Sometimes, the data might be incomplete. The data will be with blanks where information should be. The data cleaning step decides which data should be filled in this gap. It can be either filling it with appropriate data or dropping it responsibly.
2. Standardizing Formats: Data comes in different formats. If analysis is performed with different data formats, it leads to inconsistent results. Hence, to ensure consistency, all data should follow the same structure and also the same measurement units. This consistency makes the analysis easier.
3. Dealing with outliers: Outliers are the data points that don’t fit in the data range. The data cleaning process will either convert the outlier to fit into the data range or remove it.

Practical Tips

• Develop a systematic approach to data cleaning that includes the creation of reusable functions for common things.
• Use libraries such as Pandas and Scikit-leran for efficient data cleaning and preprocessing .

Neglecting Exploratory Data Analysis

In the field of data science, Exploratory Data Analysis helps us to understand data better before making assumptions and decisions. It also helps in identifying hidden patterns within the data, detecting outliers, and also to find the relationship among the variables. Neglecting EDA may miss out on important insights, which makes our analysis misguided. EDA is the first step in data analysis. To understand the data better Analysts and data scientists generate summary statistics, create visualizations, and check for patterns. EDA aims to gain insights from the underlying structure, relationships, and distributions of the variable.

Causes of Neglecting Exploratory Data Analysis

Example: Not identifying customer purchase patterns. Let’s consider the analysis of customer purchase data from an online store. The goal is to identify trends and optimize marketing strategies. Imagine that no EDA has been performed. This may miss out on seasonal trends of the product, customer demographic patterns, etc. Consequently, this will lead to suboptimal marketing strategies, and missed opportunities for increased sales.

Key Aspects of Exploratory Data Analysis

1. Data Visualization: Data Visualization is the art of presenting data in the form of graphs and other visuals. They are used to represent complex information in a more accessible and understandable manner. The common types of visualizations are Histograms, Scatter plots, and box plots.
2. Descriptive Statistics: It gives a concise summary of key features and characteristics of a dataset. Central Tendency helps to understand the average behavior of the dataset. Measures like range, variance, and standard deviation help to measure the dispersion of data. Skewness and kurtosis offer insights into the shape and symmetry of the data distribution. The correlation coefficient measures the strength and direction of the relationship between two variables.
3. Pattern Recognition: Pattern Recognition identifies meaningful relationships, trends, and structures within the data. Using EDA we can uncover recurring shapes, behaviors, and arrangements of data points. This helps in identifying underlying patterns and trends. Patterns such as seasonal patterns, cyclic patterns, trends, time series patterns, and spatial arrangements can be captured by EDA.
4. Hypothesis Generation: Hypothesis Generation in EDA refers to the initial assumptions and guesses about relationships, patterns, and trends. It proposes potential explanations for observed phenomena, leading to further investigation—identified patterns, correlation, spatial arrangements, and outlier detection help to justify the assumption.

Practical Tips

• By using the data visualization tools like Matplotlib and Seaborn to quickly explore and visualize key patterns in the data.
• Incorporate hypothesis generation during EDA to guide subsequent analyses and model building.

Ignoring Feature Scaling

In data science, Feature scaling is a preprocessing technique that transforms numerical variables measured in different units into a common unit. This facilitates robust and efficient model training. Feature scaling helps modify the magnitude of individual features and does not influence the behavior of the machine learning algorithm. Algorithms like gradient descent converge faster when numbers are on a similar scale. In the world of data, variables are the features that take different units. Scaling will adjust all different units into a single unit to make sure no single feature overpowers others just because of measuring units.

Causes of Ignoring Feature Scaling

Example: Assumption of similar scale Let’s consider a dataset with age and income variables. Age is in the range of 20 to 60 and income is in the range 10000 to 100000. If both the features are treated equally, the model will be biased towards income. So it’s essential to convert both the features to a similar scale to get accurate predictions.

Key Aspects of Feature Scaling

1. Min-Max scaling: It is a method of normalizing input features/variables. By using min-Max scaler all features are transformed to a range of 0 to 1. It means the minimum variable will be mapped to 0 and the maximum to 1.
2. Standardization: In this method, the values are centered around the mean with the unit standard deviation. It means that if the mean of an attribute becomes zero, it means the resultant distribution has a unit standard deviation.
3. Robust scaling: In some situations, the extreme values negatively impact other features of the data set. To overcome this, robust scaling uses median and Interquartile Range(IQR). It transforms all the data points within the range of median to IQR value.

Practical Tips

• By applying feature scaling consistently across numerical variables to ensure uniformity.
• Experiment with Min-Max scaling, standardization, and robust scaling to understand their impact on model performance.

Using default Hyperparameters

In the world of Data Science, algorithms can’t automatically figure out the best way to make predictions. There are certain values called hyperparameters that can be adjusted in the algorithm to get better results. Using default parameters means, using the same parameters given by the algorithm. Hyperparameters are externally set by an algorithm. Internal parameters are used while training the data. External parameters are set by the user before the training process begins. Hyperparameters influence the performance of the algorithm.

Causes of Using Default Parameters

Example: Baseline Performance assessment : Let’s consider using a decision tree for a classification task. We always use default parameters to get the initial accuracy. Then we will experiment with different values to get better results. Using the initial value, without experimenting with other values while training a model will give us poor results.

Key Aspects of Hyperparameter

1. Learning Rate: It says about the size of the steps the model takes during the optimization process. Larger learning rates will never converge whereas the smaller learning rates will take more time to converge. We should experiment with different learning rate values to get optimized results.
2. Regularization Strength: To reduce high variance we need more training data. It is expensive. Regularization can be considered as an alternate method to increase performance. In regularization, we add an extra parameter, lambda to the cost function. If lambda is zero there will be no regularization and higher lambda values correspond to more regularization.
3. Hidden Layers: It is a hyperparameter tuning method used in Neural Networks. Smaller hidden layers are enough for simpler problems whereas larger problems need more hidden layers. Using the right number of hidden layers will prevent overfitting. The number of layers can be tuned using the ‘for loop’.
4. Max depth in Decision Tree: It is the longest path between the root node and the leaf node. Increasing the depth of the tree increases the performance. On the other hand, when max_depth increases initially but after a certain point it decreases rapidly.

Practical Tips

• By conducting hyperparameter tuning using various techniques like grid search or random search to find optimal values.
• Understand the impact of key hyperparameters such as learning rate, regularization strength, hidden layers, and max depth in decision trees.

Overfitting the Model

Overfitting is a general problem in data science when a model performs too well for training data. When it sees new data it will not perform that well. The overfitting model fails in the generalization of data. Generalization of the model is essential as it performs well for both training and unseen data. The overfitting model learns the training data well. It captures noise and random fluctuations rather than capturing underlying patterns. When a model trains too long on training data or when a model is too complex, it starts learning noise and other irrelevant information. The overfitted model cannot perform well on classification and prediction tasks. Low bias (error rates) and high variance are good indicators for the overfitting model.

Causes of using the overfitting model

Example: Prediction model: Let’s consider the price prediction of houses based on their square feet. We are using a polynomial regression model to capture the relationships between square feet and prices. The model is trained well so that it fits perfectly with the training data resulting in a low error rate. But when it’s used to predict with a new set of data it results in poor accuracy.

Key Aspects of overfitting the model

1. Bias-Variance Trade-Off: Overfitting is also a part of the Bias-Variance trade-off. Complex models reduce bias but increase variance. The overfitting models have low bias and high variance leading to poor generalization.
2. Regularization: Overfitting occurs when regularizations are not applied appropriately. We can use regularization methods like L1 and L2 regularization. It penalizes overly complex models and makes them generalized.
3. Cross-Validation: Cross-validation techniques such as k-fold cross-validation will help in detecting and solving overfitting problems. It does it by evaluating the model on multiple subsets of data and provides a more robust method for generalization.

Practical Tips

• By implementing regularization techniques like L1 and L2 regularization to prevent overfitting.
• By using cross-validation methods such as k-fold cross-validation for robust model evaluation.

Not documenting the code

In data science, while working with data, code documentation acts as a helpful guide. It helps to understand the complex patterns and instructions written in the code. If there is no documentation for the code, the new user finds it difficult to understand the preprocessing steps, ensemble techniques, and feature engineering being performed in the code. Code documentation is a collection of comments and documents that explain the working of the code. Clear documentation of our code is essential to collaborate across different teams and to share codes with developers of other organizations. Spending time to document the code will make the work easier.

Causes of Not documenting the code

Example: Feature Engineering Let’s consider the feature engineering techniques used in the code. If the code doesn’t explain how the features are chosen, future iterations of the model may miss many valuable insights behind the previous feature engineering decisions.

Key Aspects of Documentation

1. Inline comments: Inline comments are like a little message the developer can include in the code. The inline comments provide extra information, context, or explanation wherever needed. Inline comments should be in plain language and they should give the descriptions in a human-friendly manner. It provides clarifications to the tricky parts of the code. We can also include reminders for future modifications or enhancements.
2. Function and Module Description: Description for a function, class, or module can be placed at the beginning part code. It will describe the purpose of that module, its parameters, and their expected outcomes. Also, we can include practical examples that help the user to understand their applications.
3. README files: README files act as a comprehensive guide for the entire project. It includes an overview of the project, the installation instructions, and usage details. Updates regarding the project can also be mentioned in this section. We can also place the directory structure in the README file.

Practical Tips

• Include inline comments to explain complex sections of code and provide context.
• Write comprehensive README files to serve as a project guide, including installation instructions and project updates.

Conclusion

In data science, insights emerge from using different algorithms and datasets. When handling information, we have the responsibility to avoid common mistakes that can occur while writing the code. Cleaning up of data and Exploratory data analysis are very essential steps while writing codes for data science. Feature scaling, using the right hyperparameters, and avoiding overfitting, will help the model to work efficiently. Proper documentation will help others to understand our code better. Our data science coding will be efficient if all the above mistakes are avoided.

Common Mistakes to Avoid in Data Science Code – FAQ’s

What is Data science?

Data science is a field that involves extracting meaningful insights from the large set of data given. Various algorithms and techniques can be used to extract hidden information.

What is Exploratory Data Analysis?

EDA is the first step in data analysis. To understand the data better Analysts and data scientists generate summary statistics, create visualizations, and check for patterns. EDA aims to gain insights from the underlying structure, relationships, and distributions of the variable.

What is Bias-Variance Trade-Off?

It is the balance between model simplicity(bias) and flexibility(variance). Lower bias and higher variance lead to model overfitting. Higher bias and lower variance will make the model underfit.

How does cross-validation help in data science?

Cross-validation techniques such as k-fold cross-validation will help in detecting and solving overfitting problems. It does it by evaluating the model on multiple subsets of data and provides a more robust method for generalization.

What are inline comments in documentation?

Inline comments are like a little message the developer can include in the code. The inline comments provide extra information, context, or explanation wherever needed. Inline comments should be in plain language and they should give the descriptions in a human-friendly manner. It provides clarifications to the tricky parts of the code. We can also include reminders for future modifications or enhancements.