Clean Data Better Models👍: Data Quality Issues ⚠️ that kill your ML Models

Introduction 👋

🤔 Have you ever trained a machine learning model only to find that it’s not performing as well as you expected? One of the most common reasons for this is poor data quality. In this article, we’ll dive into some of the data quality issues that can kill your machine-learning models and discuss practical solutions for addressing them.

Data Quality Issues ⚠️

📊 Data quality issues can take many forms, such as missing values, outliers, and inconsistent data. These issues can lead to a variety of problems when building machine learning models, such as increased bias and reduced model performance.

Missing Values⁉️

🤷‍♂️ One of the most common data quality issues is missing values. This can occur for a variety of reasons, such as data entry errors or missing data fields. When building a machine learning model, missing values can cause problems because the model won’t have enough information to make accurate predictions. 💡 One solution for addressing missing values is to use imputation techniques. These techniques use statistical methods to fill in the missing values with plausible values. For example, you can use the mean or median of the non-missing values to fill in the missing values.

Outliers ❕

📈 Outliers are values that are significantly different from the majority of the data. These values can have a big impact on machine learning models because they can skew the model’s predictions. 💡 One solution for addressing outliers is to use outlier detection techniques. These techniques use statistical methods to identify the outliers in the data. Once the outliers have been identified, you can either remove them or replace them with more plausible values.

Inconsistent Data 📉

🤯 Inconsistent data refers to data that is not in the same format or has different units of measurement. This can make it difficult to build a machine-learning model because the model won’t be able to understand the data. 💡 One solution for addressing inconsistent data is to use data-cleaning techniques. These techniques use a variety of methods to standardize the data and make it consistent. This can include things like converting all data to the same units of measurement or removing duplicate data. 🤔 I’m currently working on a personal project to predict Gaborone House Prices, I had to deal with missing values and outliers in the data. I used the mode (sometimes the mean) of the non-missing values to fill in the missing values and identified the outliers by using the Z-score method and then I removed them from the data. After cleaning the data, I trained the model and I saw a significant improvement in model performance.

Practical Solutions for the issues above in Python Code

# Imputation of Missing Values from sklearn.impute import SimpleImputer imputer = SimpleImputer(strategy='mean') X = imputer.transform(X) # Outlier Detection from scipy import stats import numpy as np z = np.abs(stats.zscore(X)) outliers = np.where(z > 3) X = X[(z < 3).all(axis=1)] # Data Cleaning import pandas as pd df = pd.read_csv("your_data.csv") df = df.apply(lambda x: x.str.strip() if x.dtype == "object" else x) df = df.drop_duplicates()

Conclusion 🏁

🤔 Data quality issues can have a big impact on the performance of your machine-learning models. By understanding the different types of data quality issues and implementing solutions to address them, you can improve the performance of your models and make more accurate predictions. In this article, we discussed missing values, outliers, and inconsistent data, and provided practical solutions for addressing each of these issues using python code. Remember to always keep an eye on your data and to clean it properly before training any model. This will save you a lot of time and frustration in the long run!

Final Thoughts 💭

This article is about data quality issues that can negatively affect the performance of machine learning models, and provides practical solutions for addressing these issues. It covers three main types of data quality issues: missing values, outliers, and inconsistent data. It explains the problems caused by these issues, and presents python code snippets for addressing them, such as imputation for missing values, outlier detection, and data cleaning. The article also includes a personal anecdote to illustrate the importance of addressing data quality issues and the impact it can have on model performance.

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