Exploratory Data Analysis and Predictive Modeling for Customer Churn Analysis

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Subject: Data Science

Category: Assignment Help

Objective:

Conduct Exploratory Data Analysis (EDA) and build a predictive model for customer churn analysis using a telecom dataset.

Part 1: Exploratory Data Analysis (EDA)

1. Data Cleaning:

• Handle Missing Values:

  • Identify columns with missing values using isnull() or info() methods.

  • Replace missing values with appropriate imputation techniques:

    • Mean/Median for numerical data.

    • Mode for categorical data.

    • Domain-specific default values or forward/backward filling for time-series data.

• Check for Duplicates and Inconsistencies:

  • Remove duplicate records using drop_duplicates().

  • Standardize categorical values (e.g., convert "Yes"/"Y"/"y" into "Yes").

• Format Data Types:

  • Ensure consistency in data types for numerical and categorical features.

  • Convert date columns into datetime format for proper analysis.

2. Data Visualization:

• Feature Distributions:

  • Use histograms to analyze the distribution of continuous variables.

  • Use box plots to detect outliers and understand variance.

• Relationship Analysis:

  • Use scatter plots to explore relationships between numerical features.

  • Create heatmaps and correlation matrices to identify highly correlated features.

• Target Variable Exploration:

  • Analyze the distribution of the target variable (e.g., churn rates) using count plots.

  • Use visualizations to compare churn rates across demographic groups, contract types, or other categorical features.

3. Insights Derivation:

• Summarize Findings:

  • Highlight features that appear significant in influencing the target variable (e.g., high churn rates for customers with monthly contracts).

• Identify Patterns:

  • Look for trends, such as whether customers with higher tenure are less likely to churn.

• Provide Actionable Insights:

  • Translate findings into recommendations (e.g., "Offer discounts to customers with monthly contracts to reduce churn rates").

Part 2: Feature Engineering

1. Feature Transformation:

• Normalize/Standardize Numerical Features:

  • Scale features like MonthlyCharges and TotalCharges to ensure comparability across models.

• Encode Categorical Features:

  • Use one-hot encoding for nominal variables like Contract (e.g., "Monthly", "Yearly").

  • Use label encoding for ordinal variables if applicable.

2. Feature Creation:

• Derived Features:

  • Calculate average monthly charges by dividing TotalCharges by tenure.

  • Create a feature for "customer age" if the data includes a sign-up date.

• Interaction Features:

  • Combine features like tenure and contract to represent long-term customers.

3. Feature Selection:

• Use techniques like correlation analysis, variance thresholding, or feature importance (e.g., from Random Forest) to select the most significant features.

Part 3: Predictive Model Development

1. Model Selection:

• Start with Logistic Regression as a baseline model for binary classification.

• Use Random Forest for a robust, interpretable model that can capture feature importance.

2. Model Training:

• Split the dataset into training and testing subsets (e.g., 80%-20%).

• Train multiple models and compare their performance using cross-validation.

3. Hyperparameter Tuning:

• Use GridSearchCV or RandomSearchCV to optimize model hyperparameters (e.g., number of trees in Random Forest).

4. Model Interpretation:

• For Logistic Regression, analyze coefficients to understand feature impact.

• For Random Forest, use feature importance scores for insights.

Part 4: Model Evaluation

1. Metrics Calculation:

• Accuracy: Overall performance metric.

• Precision: Focus on correctly identifying churned customers.

• Recall: Measure of capturing all actual churned customers.

• F1-Score: Balance between precision and recall.

• ROC-AUC: Compare models’ performance using Receiver Operating Characteristic curves.

2. Cross-Validation:

• Use k-fold cross-validation to ensure model robustness and avoid overfitting.

3. Insights from Evaluation:

• Summarize findings, e.g., "Random Forest achieved the best F1-score of 0.85, indicating a good balance between precision and recall."

• Highlight areas for improvement (e.g., "Additional features or data may further improve recall").

Part 5: Deliverables

1. EDA Report:

   • Summarize data cleaning, visualizations, and insights.

2. Feature Engineering Summary:

   • Highlight new features and their potential impact.

3. Predictive Model Code:

   • Provide well-commented Python code or Jupyter Notebook.

4. Model Evaluation Results:

   • Include metrics and final recommendations for improving churn rates.

This expanded format provides detailed guidance for each task and makes the assignment easy to follow.

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