Week 1 Project: Churn prediction for sprint

Imagine you're working with Sprint, one of the biggest telecom companies in the USA. They're really keen on figuring out how many customers might decide to leave them in the coming months. Luckily, they've got a bunch of past data about when customers have left before, as well as info about who these customers are, what they've bought, and other things like that.

So, if you were in charge of predicting customer churn how would you go about using machine learning to make a good guess about which customers might leave? Like, what steps would you take to create a machine learning model that can predict if someone's going to leave or not?

Solution Predicting customer churn is a critical task for telecom companies like Sprint. Machine learning can be a powerful tool to build predictive models that can identify potential churners. Here's a step-by-step approach to create a machine learning model for predicting customer churn:

1. Data Collection and Preprocessing:
   • Gather historical data on customer churn, which should include features like customer demographics, usage patterns, billing information, customer service interactions, and the churn status (churned or not).
   • Clean and preprocess the data to handle missing values, outliers, and categorical variables. Convert categorical variables into numerical representations (e.g., one-hot encoding or label encoding).

import pyforest
data = pd.read_csv("C:\\Users\\FIREBOYY\\Downloads\\sprint.csv")

2. Data Exploration and Analysis:
   • Perform exploratory data analysis (EDA) to gain insights into the dataset. This can involve visualizations and statistical summaries to understand the distribution of features and their relationships with churn.
   • Identify key features that are likely to be strong predictors of churn.

data.dropna(inplace = True)
# Encode categorical variables (assuming 'gender' is a categorical variable)
data = pd.get_dummies(data, columns=['gender'])

# Define features
X = data  # Features

# Standardize numerical features (optional, but often recommended)
scaler = MinMaxScaler()
X[['age', 'total_day_minutes', 'total_eve_minutes', 'total_night_minutes',
   'total_intl_minutes', 'total_day_calls', 'total_eve_calls', 'total_night_calls']] = scaler.fit_transform(X[['age', 'total_day_minutes', 'total_eve_minutes', 'total_night_minutes',
   'total_intl_minutes', 'total_day_calls', 'total_eve_calls', 'total_night_calls']])

3. Feature Engineering:

   • Create new features or transform existing ones that might improve the predictive power of the model. For example, you can calculate customer tenure, customer lifetime value, or churn history.

4. Data Splitting:

   • Split the dataset into training, validation, and test sets. Typically, you might use a 70-20-10 or 80-10-10 split.

5. Model Selection:

   • Choose an appropriate machine learning algorithm for classification tasks. Common choices include logistic regression, decision trees, random forests, gradient boosting, support vector machines, and neural networks.
   • Experiment with multiple algorithms to determine which one performs best for your specific dataset.

# Import necessary libraries
from sklearn.model_selection import train_test_split
import xgboost as xgb
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import accuracy_score,precision_score, recall_score, f1_score, roc_auc_score


# Data preprocessing
# You'd typically perform more extensive data preprocessing here
# including handling missing values, encoding categorical variables, etc.
data.dropna(inplace = True)
# Encode categorical variables (assuming 'gender' is a categorical variable)


# Define features and target variable
X = data.drop("churn_status", axis=1)  # Features
y = data["churn_status"]  # Target variable
# Split the dataset into training and testing sets
X_train, X_test = train_test_split(X,y, test_size=0.2, random_state=42)

# Initialize and train an XGBoost classifier model
model = xgb.XGBClassifier(random_state=42)
model.fit(X_train, y_train)

# Make predictions on the test set
y_pred = model.predict(X_test)

6. Model Training:

   • Train the selected models on the training dataset using appropriate hyperparameters.
   • Implement techniques like cross-validation to assess the model's performance and fine-tune hyperparameters.

7. Evaluation Metrics:

   • Select appropriate evaluation metrics for your churn prediction model. Common metrics include accuracy, precision, recall, F1-score, and area under the ROC curve (AUC-ROC).
   • Consider the business context and the cost associated with false positives and false negatives when choosing evaluation metrics.

8. Model Evaluation:

   • Evaluate the model's performance on the validation set. Tune hyperparameters and adjust the model as needed to improve its performance.
   • Use techniques like feature importance analysis to understand which features are driving predictions.

9. Final Model Selection:

   • Select the best-performing model based on validation metrics.

10. Model Testing:

    • Evaluate the final model on the test dataset to get an unbiased estimate of its performance.

11. Deployment:

    • Deploy the trained model into a production environment where it can make real-time predictions on new customer data.

12. Monitoring and Maintenance:

    • Continuously monitor the model's performance in the production environment. Retrain the model periodically as data evolves and customer behavior changes.

13. Actionable Insights:

    • Translate model predictions into actionable insights. Identify high-risk customers and develop retention strategies or offers to prevent churn.

14. Feedback Loop:

    • Implement a feedback loop to incorporate the results of your retention strategies back into the data pipeline. This can help improve the model's predictions over time.

Predicting customer churn is an ongoing process that requires constant monitoring, model refinement, and proactive customer engagement based on the insights gained from the model's predictions.