Data Science Project Generator

### Project Blueprint: Customer Churn Prediction for Telecommunication Companies #### **1. Objectives** - **Primary Objective:** Accurately predict customer churn to enable proactive retention strategies. - **Secondary Objectives:** - Identify key factors contributing to churn. - Segment customers based on churn risk and associated attributes. - Develop actionable recommendations to reduce churn and improve customer satisfaction. --- #### **2. Data Sources** - **Customer Data:** - Demographic details (age, gender, location, etc.). - Contract information (type, duration, payment method). - Account status (tenure, billing history, overdue payments). - **Service Usage Data:** - Call data records (duration, frequency, types of calls). - Internet usage (bandwidth, data consumption patterns). - Subscription details (plans, upgrades, or downgrades). - **Customer Interaction Data:** - Complaint records. - Customer support tickets and resolution times. - Satisfaction survey responses. - **External Data (Optional):** - Market trends and competition analysis. - Social media sentiment analysis related to the company. --- #### **3. Recommended Preprocessing Steps** 1. **Data Cleaning:** - Handle missing values using imputation techniques (mean/median/mode for numerical, most frequent category for categorical). - Remove duplicate or inconsistent records. - Normalize inconsistent formats (e.g., date formats). 2. **Feature Engineering:** - Create derived metrics such as average revenue per user (ARPU), churn score, etc. - Encode categorical variables using one-hot encoding or label encoding. - Bin continuous variables into meaningful groups if necessary (e.g., age brackets, tenure buckets). 3. **Outlier Detection:** - Use boxplots or z-score methods to identify outliers and decide on treatment strategies (cap, remove, or transform). 4. **Data Balancing:** - Address class imbalance using techniques like SMOTE (Synthetic Minority Oversampling Technique) or cost-sensitive learning. 5. **Scaling:** - Standardize or normalize numerical features for algorithms sensitive to feature scales (e.g., Logistic Regression, SVM). --- #### **4. Key Analysis Methods** 1. **Exploratory Data Analysis (EDA):** - Visualize customer distributions, correlations, and patterns using tools like Matplotlib, Seaborn, or Tableau. - Identify trends in churn rates by demographics, service usage, and other attributes. 2. **Feature Selection:** - Use statistical methods (e.g., Chi-square test, ANOVA) and feature importance techniques (e.g., from Random Forest, SHAP values). 3. **Predictive Modeling:** - Train machine learning models such as: - Logistic Regression (baseline model for interpretability). - Decision Trees or Random Forests. - Gradient Boosting Machines (XGBoost, LightGBM, or CatBoost). - Neural Networks (for complex, high-dimensional data). 4. **Model Evaluation:** - Evaluate model performance using metrics like accuracy, precision, recall, F1-score, and ROC-AUC. - Perform cross-validation to ensure robustness and avoid overfitting. 5. **Churn Risk Segmentation:** - Use clustering algorithms (e.g., K-Means, DBSCAN) to group customers based on risk levels and contributing factors. --- #### **5. Tools and Technologies** - **Programming Languages:** - Python (libraries: Pandas, NumPy, Scikit-learn, TensorFlow, Keras, XGBoost, SHAP). - R (for statistical analysis and visualization). - **Data Visualization:** - Tableau, Power BI, or Python (Matplotlib, Seaborn, Plotly). - **Big Data Processing (if applicable):** - Apache Spark or Hadoop for large-scale datasets. - **Cloud Platforms:** - AWS (SageMaker), Google Cloud (Vertex AI), or Microsoft Azure (ML Studio). - **Version Control and Collaboration:** - Git, GitHub, or GitLab. --- #### **6. Presentation and Insights Communication** 1. **Executive Summary:** - Present key findings in simple terms, focusing on high-impact insights. - Highlight actionable recommendations to reduce churn. 2. **Data Visualization:** - Use dashboards to present churn trends, segment analysis, and predictive model outputs interactively. - Include visual explanations for model interpretability (e.g., SHAP plots or LIME results). 3. **Customer Profiles:** - Show representative profiles for high-risk churn customers with recommended actions. 4. **Impact Simulation:** - Provide "what-if" analysis to demonstrate the potential business impact of implementing retention strategies. 5. **Technical Report (Optional):** - Share detailed methodology, data preprocessing steps, and model performance metrics for technical stakeholders. --- This blueprint provides a structured approach to developing a churn prediction system for telecommunications. Each step ensures a balance between technical rigor and practical business application.