Avoid These Top 5 Mistakes in Data Science Projects

Author(s): Simranjeet Singh

Originally published on Towards AI.

Introduction

In recent years, the field of data science has grown in importance, and a variety of businesses are now using it. Data science is now a crucial tool for businesses to use when making decisions based on data analysis across industries, including healthcare, finance, and retail. A project’s success might be hampered by typical errors, even though data science has the ability to reveal insightful information.

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The goal is to identify five errors that frequently occur in data science initiatives and offer advice on how to prevent them. You may raise the accuracy of your analysis and the likelihood that your data science projects will be successful by being aware of and avoiding these mistakes.

What Experts Say about Project Life Cycle?

“Data science is a large, interdisciplinary field, and success requires a diverse skill set and a willingness to be wrong most of the time,” the data scientist Hadley Wickham remarked. You can improve the accuracy and success of your data science efforts by accepting the risk of inaccuracy and avoiding typical traps.

Imagine, for instance, that a hospital is attempting to forecast patient readmission rates based on a variety of variables, including age, prior medical history, and duration of stay. If the problem statement is poorly formulated, the analysis might not yield any insightful conclusions, and the hospital might still have high readmission rates. The analysis, on the other hand, can offer useful insights that help lower readmission rates and improve patient outcomes if the problem statement is well stated.

Mistake #1: Lack of Clear Problem Statement

Defining problem statement is the first step in any data science project because it shows what you want to do. If problem statement is not clear that will make it difficult to know what to do and this will cause wastage of time and resources.

A problem statement is a clear and concise description of the problem you’re trying to solve. It should be specific, measurable, and achievable. By defining the problem statement clearly, you can ensure that the analysis is focused and provides efficient and data-driven insights.

Issues:

1. When problem statements are not clearly defined, common issues arise. Assume a company wants to improve sales by analysing customer data. The analysis may become too broad and fail to identify specific areas for improvement if there is no clear problem statement.
2. When problem statements are not clearly defined, the analysis may be based on incomplete or irrelevant data. As a result, incorrect conclusions and ineffective solutions may result.

For Example,

Consider the following scenario: a school district wishes to improve student performance. A lack of a clear problem statement may lead the district to analyse test scores for all students without taking demographics, socioeconomic status, and teaching quality into account. The analysis may reveal that some students are performing poorly, but the district may not know how to improve performance without a clear problem statement.

Correcting this error entails clearly defining the problem statement from the start. This entails asking questions like, “What is the specific problem we’re attempting to solve?” as well as “What data do we need to analyse in order to solve this problem?” You can ensure that the analysis is focused and provides actionable insights that help solve the problem at hand by clearly defining the problem statement.

Mistake #2: Poor Data Cleaning

Cleaning data is an essential step in any data science project. It entails removing or correcting data errors, inconsistencies, and inaccuracies to ensure that the analysis is founded on reliable data.

Because inaccurate or incomplete data can lead to incorrect conclusions and ineffective solutions, data cleaning is critical. Suppose a company wants to analyse customer satisfaction based on data collected in survey results. If the survey data is not properly cleaned, the analysis based on incorrect or incomplete responses, resulting in incorrect results and solutions.

Common data cleaning mistakes include:

1. Duplicate data can result in skewed results, it is critical to remove duplicates from the dataset.
2. Missing values can distort analysis results, so they must be imputed correctly.
3. Incorrectly formatted data can lead to errors in analysis, it is critical to ensure that data is formatted correctly.

Consider the following coding snippet, which demonstrates an incorrect approach to data cleaning:

# Incorrect approach to cleaning data
import pandas as pd

data = pd.read_csv('survey_data.csv')
data.drop_duplicates()

The data is read from a CSV file and duplicates are removed in the above code snippet. The cleaned data, however, is not saved back to the original data frame, so the original dataset remains unchanged.
A better way to remove duplicates would be:

# Corrected approach to removing duplicates
import pandas as pd

data = pd.read_csv('survey_data.csv')
data = data.drop_duplicates()

The drop duplicates() method is applied to the data data frame in the corrected code snippet, and the cleaned data is saved back to the original data frame.

You can avoid errors and inaccuracies in your analysis by ensuring proper data cleaning, resulting in more accurate conclusions and effective solutions.

Mistake #3: Overfitting and Underfitting

In data science projects, overfitting and underfitting are common errors. Overfitting occurs when a model is overly complex and closely matches the training data, resulting in poor performance on new data. Underfitting occurs when a model is overly simple and unable to capture the complexities of the data, resulting in poor performance.

Example of overfitting

Overfitting occurs when we create a model that perfectly fits the training data but performs poorly on new data. Assume we have a dataset of student grades with multiple attributes such as age, gender, study hours, and final grade. Overfitting occurs when we create a model that uses all of the features and perfectly fits the training data but performs poorly in predicting the grade of new students.

Example of underfitting

Underfitting occurs when we create a model that is too simple and cannot capture the complexity of the data. Assume we have a dataset of student grades with multiple attributes such as age, gender, study hours, and final grade. A model that only uses age as a feature is overly simplistic and cannot accurately predict the grade of new students.

Wrong approach code snippet for overfitting:

from sklearn.linear_model import LinearRegression
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
regressor = LinearRegression()
regressor.fit(X_train, y_train)
y_pred = regressor.predict(X_test)

Because it employs a linear regression model, which may not capture the complexities of the data, this code snippet does not prevent overfitting.

Right approach code snippet for overfitting:

from sklearn.ensemble import RandomForestRegressor
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
regressor = RandomForestRegressor(n_estimators=100, random_state=0)
regressor.fit(X_train, y_train)
y_pred = regressor.predict(X_test)

This code snippet implements a random forest regression model, which is capable of handling complex data while avoiding overfitting.

Wrong approach code snippet for underfitting:

from sklearn.linear_model import LinearRegression
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
regressor = LinearRegression()
regressor.fit(X_train[['age']], y_train)
y_pred = regressor.predict(X_test[['age']])

This code snippet only uses age as a feature, which is overly simplistic and could result in underfitting.

Right approach code snippet for underfitting:

from sklearn.linear_model import LinearRegression
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
regressor = LinearRegression()
regressor.fit(X_train, y_train)
y_pred = regressor.predict(X_test)

This code snippet employs a linear regression model that can handle data and avoid underfitting.

Mistake #4: Ignoring Data Quality

Ignoring data quality is a common error in data science projects, which can result in inaccurate results. Before using data for analysis, it is critical to ensure that it is accurate, complete, and error-free. It would be a mistake to simply ignore missing values or outliers in the data. Before analysing the data, it is best to check for errors, fill in missing values, and remove outliers.

Wrong Approach of handling missing values that impact the data quality:

# Wrong approach: ignoring missing values
X_train.dropna(inplace=True)

Right approach to handle the missing value by doing imputation:

# Right approach: filling missing values with mean
from sklearn.impute import SimpleImputer
imputer = SimpleImputer(strategy='mean')
X_train_imputed = imputer.fit_transform(X_train[['feature1', 'feature2']])

Follow these steps to ensure data quality in data science projects:

1. Examine the data for missing values, outliers, and errors.
2. Remove or fill in any missing values as needed.
3. Outliers and errors should be handled by removing them or replacing them with appropriate values.
4. To eliminate scaling issues, standardise or normalise the data.
5. Use domain knowledge to identify and resolve data inconsistencies.
6. Check for consistency and accuracy when validating the data.
7. To identify patterns and relationships in the data, use appropriate data visualisation techniques.

Mistake #5: Poor Communication

Miscommunication and delays in data science projects can be caused by poor communication and collaboration. It is critical for team members to communicate and collaborate effectively in order to ensure that everyone is on the same page and working towards the same goals. Working in silos and not communicating with other team members is a bad strategy.

A good strategy would be to hold regular meetings to discuss progress and share ideas, to use collaboration tools to share files and collaborate on tasks, and to define clear roles and responsibilities for each team member.

The incorrect approach is to avoid communicating with other team members.

Each member of the team works independently and does not share their progress with others.

The correct approach is to hold regular meetings and to use collaboration tools.

Members of the team meet weekly to discuss progress and use collaboration tools such as Google Drive and Trello to share files and collaborate on tasks. They also define each team member’s roles and responsibilities to avoid confusion.

Final Thoughts

Avoiding these errors is critical because they can result in inaccurate results, wasted time and resources, and ultimately failure to meet the project’s objectives.

To avoid making these errors, it is critical to clearly define the problem, collect sufficient and high-quality data, apply appropriate modelling techniques, validate the data, and communicate effectively with team members. Data science projects can be more successful and produce more accurate and meaningful results if best practises are followed.

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Published via Towards AI