Neglecting Exploratory Data Analysis

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....

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....

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....

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....

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....

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....

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

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