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Create a list of project milestones
Price range: €12.15 through €15.78Milestones for 6-Month AI Project: Customer Churn Prediction
Month 1: Project Initialization and Data Collection
- Milestone 1: Define Project Scope and Objectives
- Clearly define the business problem (customer churn prediction) and success criteria.
- Outline specific goals: Predict churn probability, identify at-risk customers, improve retention strategies.
- Milestone 2: Collect and Clean Data
- Gather historical customer data, including demographics, transaction history, customer interactions, and churn labels.
- Perform initial data cleaning: handle missing values, correct inconsistencies, and remove duplicates.
- Milestone 3: Data Exploration and Preprocessing
- Conduct exploratory data analysis (EDA) to understand distributions, correlations, and key patterns.
- Preprocess the data: feature scaling, one-hot encoding, categorical variable transformation, and feature selection.
Month 2: Feature Engineering and Model Selection
- Milestone 4: Feature Engineering
- Create new features based on domain knowledge, such as customer tenure, usage frequency, and customer service interactions.
- Use techniques like interaction terms, feature encoding, and aggregation to improve model input.
- Milestone 5: Select Initial Machine Learning Models
- Evaluate various classification models such as Logistic Regression, Decision Trees, Random Forests, and Gradient Boosting.
- Select a baseline model to establish initial performance metrics.
Month 3: Model Training and Hyperparameter Tuning
- Milestone 6: Model Training
- Train the selected models using the prepared training dataset.
- Evaluate initial performance using metrics like accuracy, precision, recall, and F1-score on the validation set.
- Milestone 7: Hyperparameter Tuning
- Use cross-validation and grid/random search techniques to optimize hyperparameters (e.g., number of trees for Random Forest, max depth, learning rate for Gradient Boosting).
- Monitor overfitting and adjust model complexity.
Month 4: Model Evaluation and Iteration
- Milestone 8: Model Evaluation
- Evaluate models on a hold-out test dataset to assess generalization and avoid overfitting.
- Compare different models’ performance using precision, recall, ROC-AUC, and F1-score.
- Analyze performance in terms of business impact, such as identifying the most at-risk customer segments.
- Milestone 9: Model Refinement
- Refine the model based on performance results. This may involve further feature engineering, removing irrelevant features, or retraining models with adjusted hyperparameters.
Month 5: Model Deployment Preparation and Integration
- Milestone 10: Model Interpretability and Validation
- Assess model explainability using tools like SHAP or LIME to understand feature importance and ensure the model’s decisions are interpretable.
- Validate the model with business stakeholders to ensure the predictions align with operational needs and objectives.
- Milestone 11: Prepare for Model Deployment
- Develop scripts and pipelines for integrating the churn prediction model into the production environment.
- Create a monitoring system to track the model’s performance post-deployment (e.g., retraining schedules, feedback loops).
Month 6: Model Deployment and Final Reporting
- Milestone 12: Model Deployment
- Deploy the model to a production environment where it can provide real-time predictions on customer churn.
- Ensure the model is integrated with customer relationship management (CRM) tools or other business platforms for actionable insights.
- Milestone 13: Final Reporting and Documentation
- Prepare comprehensive documentation detailing the model’s development, performance, and deployment.
- Present a final report summarizing the project’s objectives, milestones, evaluation results, and recommendations for improving customer retention.
- Milestone 14: Post-Deployment Monitoring and Maintenance
- Set up a post-deployment monitoring system to track the model’s performance over time.
- Schedule periodic model evaluations and retraining based on new data and business requirements.
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Write a data preprocessing checklist
Price range: €11.21 through €16.15Data Preprocessing Checklist for Classification ML Model
- Understand the Data:
- Review the dataset and understand the problem you’re solving.
- Identify the features (independent variables) and target (dependent variable).
- Check the type of problem (binary or multi-class classification) and the distribution of the target variable.
- Handle Missing Data:
- Missing Features: Check for any missing values in the features and decide whether to:
- Impute missing values (e.g., using mean, median, mode, or a predictive model).
- Drop rows/columns with missing data if they are non-essential.
- Missing Target: Ensure the target variable does not contain missing values; otherwise, remove or impute them.
- Missing Features: Check for any missing values in the features and decide whether to:
- Data Cleaning:
- Remove Duplicates: Identify and remove duplicate rows to avoid biased model training.
- Handle Outliers: Identify and address outliers in the features that may skew results. Methods include removing or transforming outliers based on domain knowledge.
- Correct Inconsistent Data: Standardize the format (e.g., date format, categorical variables) and remove any inconsistencies in the data entries.
- Encode Categorical Data:
- Label Encoding: For ordinal features where the order matters, use label encoding (e.g., low=0, medium=1, high=2).
- One-Hot Encoding: For nominal categorical features where the order does not matter, use one-hot encoding to create binary columns for each category.
- Handle Rare Categories: Merge rare categories into a single “Other” category if necessary.
- Feature Scaling:
- Standardization: Apply standardization (z-score) for models sensitive to feature scaling (e.g., logistic regression, SVM, k-NN). This centers features around 0 with unit variance.
- Normalization: Apply normalization (min-max scaling) when features need to be scaled to a specific range (0-1).
- Robust Scaling: For data with outliers, use robust scaling that uses median and interquartile range instead of mean and standard deviation.
- Feature Engineering:
- Create New Features: Based on domain knowledge, create new features (e.g., extracting date/time features like day of the week, month).
- Interaction Terms: Consider creating interaction terms between features if you suspect relationships between them could improve predictive performance.
- Feature Selection: Remove highly correlated features using methods like correlation matrix or feature importance to avoid multicollinearity and improve model efficiency.
- Data Splitting:
- Training and Test Split: Split the dataset into training and testing sets, typically a 70/30 or 80/20 ratio.
- Validation Split: Consider using a validation set or cross-validation techniques to tune hyperparameters and prevent overfitting.
- Handle Class Imbalance (If Applicable):
- Resampling Methods: For imbalanced classes, consider using oversampling (e.g., SMOTE) or undersampling to balance the dataset.
- Class Weights: For certain models (e.g., decision trees, logistic regression), you can adjust class weights to account for imbalanced classes.
- Synthetic Data Generation: In some cases, generate synthetic data for underrepresented classes using techniques like SMOTE (Synthetic Minority Over-sampling Technique).
- Verify Data Integrity:
- Ensure No Data Leakage: Confirm that no information from the test set has been included in the training process to avoid overfitting and ensure model generalization.
- Consistency Across Features: Ensure that the feature columns are consistent between training and testing datasets (e.g., identical column names, same data types).
- Final Data Review:
- Sanity Check: Perform a final check to ensure all preprocessing steps have been completed correctly and that the dataset is ready for model training.
- Visual Inspection: If possible, visualize the data (e.g., using histograms or box plots) to confirm that the preprocessing steps were successful and that the data distribution is reasonable.
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