In today’s competitive market, understanding why customers leave a platform is crucial for improving retention strategies. In this project, I performed a churn analysis to identify key drivers of customer churn and build a predictive model that helps businesses take proactive action.
🧹 Step 1: Data Preparation
The first step was to load the dataset and explore its structure using pandas. I checked for missing values and handled them as follows:
-
Categorical variables were replaced with
'NA' -
Numerical variables were filled with
0.0
This ensured a clean dataset with no missing entries that could distort model training.
📊 Step 2: Exploratory Data Analysis (EDA)
EDA helped uncover important patterns and relationships:
- Visualized churn distribution to check for class imbalance.
- Used correlation heatmaps to understand feature relationships.
- Examined key attributes such as tenure, contract type, and monthly charges, which appeared to influence churn the most.
⚙️ Step 3: Feature Engineering
Categorical variables like InternetService, Contract, and PaymentMethod were encoded using one-hot encoding.
This allowed the model to interpret categorical information numerically while preserving feature richness.
🤖 Step 4: Model Building – Logistic Regression
I chose Logistic Regression as the baseline model for its interpretability and reliability in binary classification tasks.
Using Scikit-learn:
model = LogisticRegression(solver='liblinear', C=1.0, max_iter=1000, random_state=42)
After splitting the data into train, validation, and test sets (60/20/20), the model was trained to predict the likelihood of churn.
Validation Accuracy: 0.68
This means the model correctly predicted customer churn status 68% of the time — a solid start for a baseline model without feature tuning or balancing.
🔍 Step 5: Feature Importance and Insights
By analyzing model coefficients, I identified the top drivers of churn:
- High Monthly Charges — customers paying more are more likely to churn.
- Shorter Tenure — newer customers tend to leave earlier.
- Month-to-Month Contracts — less commitment leads to higher churn risk.
These insights can help businesses design targeted retention strategies such as offering discounts or loyalty rewards for high-risk segments.
🧠 Step 6: Regularization and Model Optimization
I further experimented with different regularization strengths (C = [0.01, 0.1, 1, 10, 100]) to find the optimal balance between bias and variance.
This step stabilized the model and prevented overfitting while slightly improving accuracy.
✅ Key Takeaways
- Data cleaning and proper encoding are crucial for reliable model performance.
- Logistic Regression provides not just predictions but interpretable insights.
- Churn analysis can directly inform business retention strategies through data-backed decisions.
💬 Final Thoughts
This churn analysis project reinforced the power of data-driven decision-making. By transforming raw customer data into actionable insights, businesses can not only predict who might churn — but also understand why.
In the future, I plan to enhance this model using ensemble methods like Random Forest or XGBoost to capture nonlinear relationships and further improve accuracy.
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