How can I identify trends and patterns in the data like subscriber data of a telecom company?

 In the context of Salesforce, you can use the various tools and techniques for your particular objective of getting the patterns and trends from a data set of a telecom company. Here are the points given:-

Data exploration

You can start by loading the subscriber data into a data analysis tool in the Python programming language such as pandas and NumPy. You can utilize descriptive statistics to understand the variables’ distribution:-

Import pandas as pd

# Load subscriber data

Subscriber_data = pd.read_csv(‘subscriber_data.csv’)

# Explore data

Print(subscriber_data.describe())

Print(subscriber_data.head())

Data visualization

You can perform data visualization like Matplotlib or Seaborn so that you can gain insights quickly:-

Import matplotlib.pyplot as plt

Import seaborn as sns

# Visualize subscriber demographics

Sns.histplot(subscriber_data[‘age’], bins=20, kde=True)

Plt.title(‘Distribution of Subscriber Age’)

Plt.xlabel(‘Age’)

Plt.ylabel(‘Frequency’)

Plt.show()

Churn analysis

You can also calculate the churn rate which refers to the percentage of subscribers who left their services:-

# Calculate churn rate

Churn_rate = (subscriber_data[‘churned’].sum() / len(subscriber_data)) * 100

Print(“Churn Rate:”, churn_rate, “%”)

# Logistic regression example

From sklearn.linear_model import LogisticRegression

From sklearn.model_selection import train_test_split

From sklearn.metrics import accuracy_score

# Prepare data

X = subscriber_data[[‘age’, ‘usage’, ‘location’]]

Y = subscriber_data[‘churned’]

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

# Train model

Model = LogisticRegression()

Model.fit(X_train, y_train)

# Predict and evaluate

Predictions = model.predict(X_test)

Accuracy = accuracy_score(y_test, predictions)

Print(“Accuracy:”, accuracy)

Segmentation analysis

You can perform analysis based on certain segmentation such as heavy users or light users or based on demographics such as groups, location, etc:-

# Segment subscribers based on usage

Subscriber_data[‘usage_segment’] = pd.cut(subscriber_data[‘usage’], bins=[0, 100, 500, float(‘inf’)], labels=[‘Low’, ‘Medium’, ‘High’])

# Segment visualization

Sns.countplot(x=’usage_segment’, hue=’churned’, data=subscriber_data)

Plt.title(‘Churn Rate by Usage Segment’)

Plt.xlabel(‘Usage Segment’)

Plt.ylabel(‘Count’)

Plt.show()

Predicative modeling

You can use the machine learning algorithms for getting the prediction of churn:-

# Random forest example

From sklearn.ensemble import RandomForestClassifier# Train model

Rf_model = RandomForestClassifier(n_estimators=100, random_state=42)

Rf_model.fit(X_train, y_train)

# Predict and evaluate

Rf_predictions = rf_model.predict(X_test)

Rf_accuracy = accuracy_score(y_test, rf_predictions)

  Print(“Random Forest Accuracy:”, rf_accuracy)