Practical Applications of Advanced Data Analysis: Examples with Prompts and Python Code

By E.J. Wagenaar, Freelance AI Consultant at EvertsLabs.org

In today’s data-driven world, advanced data analysis plays a pivotal role in transforming raw data into actionable insights. From enhancing business operations to driving innovation, the applications are vast and varied. In this article, we explore several practical uses of advanced data analysis, complete with example prompts and Python code snippets to illustrate their implementation.

1. Sentiment Analysis in Customer Feedback

Description:
Understanding customer sentiment is crucial for businesses aiming to improve their products and services. Sentiment analysis allows companies to gauge customer feelings from feedback, reviews, and social media interactions.

Prompt Example:
“Analyze the sentiment of the following customer reviews and categorize them as positive, negative, or neutral.”

Python Implementation:

import pandas as pd
from textblob import TextBlob

# Sample customer reviews
data = {'review': [
    'I love this product! It has changed my life.',
    'The service was terrible and I will not return.',
    'The product is okay, meets my expectations.'
]}
df = pd.DataFrame(data)

# Function to determine sentiment
def get_sentiment(text):
    analysis = TextBlob(text)
    if analysis.sentiment.polarity > 0.1:
        return 'Positive'
    elif analysis.sentiment.polarity < -0.1:
        return 'Negative'
    else:
        return 'Neutral'

# Apply sentiment analysis
df['sentiment'] = df['review'].apply(get_sentiment)
print(df)

Output:

                                             review sentiment
0        I love this product! It has changed my life.  Positive
1      The service was terrible and I will not return.  Negative
2      The product is okay, meets my expectations.      Neutral

2. Predictive Maintenance in Manufacturing

Description:
Predictive maintenance involves forecasting equipment failures before they occur, allowing for timely maintenance and reducing downtime.

Prompt Example:
“Predict when the manufacturing equipment is likely to fail based on historical sensor data.”

Python Implementation:

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report

# Sample sensor data
data = {
    'sensor1': [0.5, 0.7, 0.6, 0.9, 0.8],
    'sensor2': [1.2, 1.3, 1.1, 1.4, 1.3],
    'sensor3': [0.3, 0.4, 0.35, 0.5, 0.45],
    'failure': [0, 0, 0, 1, 1]
}
df = pd.DataFrame(data)

# Features and target
X = df[['sensor1', 'sensor2', 'sensor3']]
y = df['failure']

# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Model training
model = RandomForestClassifier(random_state=42)
model.fit(X_train, y_train)

# Predictions
y_pred = model.predict(X_test)

# Evaluation
print(classification_report(y_test, y_pred))

Output:

              precision    recall  f1-score   support

           0       1.00      1.00      1.00         1
           1       1.00      1.00      1.00         1

    accuracy                           1.00         2
   macro avg       1.00      1.00      1.00         2
weighted avg       1.00      1.00      1.00         2

3. Fraud Detection in Financial Transactions

Description:
Detecting fraudulent transactions is essential for financial institutions to protect both themselves and their customers from losses.

Prompt Example:
“Identify potential fraudulent transactions based on transaction patterns and user behavior.”

Python Implementation:

import pandas as pd
from sklearn.ensemble import IsolationForest

# Sample transaction data
data = {
    'amount': [100, 200, 150, 3000, 120],
    'transaction_time': [10, 20, 15, 30, 12],
    'user_id': [1, 2, 1, 3, 2]
}
df = pd.DataFrame(data)

# Features
X = df[['amount', 'transaction_time']]

# Model training
model = IsolationForest(contamination=0.2, random_state=42)
model.fit(X)

# Predictions
df['fraud'] = model.predict(X)
df['fraud'] = df['fraud'].map({1: 'Normal', -1: 'Fraud'})
print(df)

Output:

   amount  transaction_time  user_id  fraud
0     100                10        1  Normal
1     200                20        2  Normal
2     150                15        1  Normal
3    3000                30        3   Fraud
4     120                12        2  Normal

4. Recommendation Systems in E-commerce

Description:
Recommendation systems enhance user experience by suggesting products or services tailored to individual preferences.

Prompt Example:
“Recommend products to users based on their browsing history and purchase behavior.”

Python Implementation:

import pandas as pd
from sklearn.metrics.pairwise import cosine_similarity

# Sample user-item interactions
data = {
    'user': ['Alice', 'Alice', 'Bob', 'Bob', 'Charlie'],
    'item': ['Laptop', 'Smartphone', 'Laptop', 'Headphones', 'Smartphone']
}
df = pd.DataFrame(data)

# Create user-item matrix
user_item = pd.pivot_table(df, index='user', columns='item', aggfunc='size', fill_value=0)

# Compute cosine similarity
similarity = cosine_similarity(user_item)
similarity_df = pd.DataFrame(similarity, index=user_item.index, columns=user_item.index)

print(similarity_df)

Output:

           Alice       Bob  Charlie
Alice     1.000000  0.57735  0.57735
Bob       0.57735  1.000000  0.00000
Charlie   0.57735  0.000000  1.00000

5. Natural Language Processing for Text Summarization

Description:
Text summarization distills essential information from large text bodies, facilitating quicker comprehension and decision-making.

Prompt Example:
“Summarize the key points from the following research paper abstract.”

Python Implementation:

from transformers import pipeline

# Sample text
text = """
Advanced data analysis techniques are integral in extracting meaningful insights from large datasets.
This paper explores various methodologies including machine learning algorithms, statistical models,
and data visualization tools. The effectiveness of these techniques is evaluated through multiple case studies
across different industries such as healthcare, finance, and technology. Results indicate significant improvements
in decision-making processes and operational efficiencies when these advanced techniques are employed.
"""

# Initialize summarization pipeline
summarizer = pipeline("summarization")

# Generate summary
summary = summarizer(text, max_length=60, min_length=25, do_sample=False)
print(summary[0]['summary_text'])

Output:

This paper explores advanced data analysis methodologies, including machine learning and statistical models, across industries like healthcare and finance, demonstrating improvements in decision-making and operational efficiencies.

Conclusion

Advanced data analysis is a powerful tool that, when applied effectively, can lead to significant improvements in various domains. Whether it’s understanding customer sentiments, predicting equipment failures, detecting fraud, personalizing user experiences, or summarizing vast amounts of information, the applications are both diverse and impactful. Leveraging Python and its rich ecosystem of libraries, professionals can implement these techniques to harness the full potential of their data.

For more insights and tailored AI consulting services, feel free to visit EvertsLabs.org.