Prepare the data
Collect CRM, billing and support-event features, define a clear label window for renewal, remove duplicates and handle missing fields before splitting.
01 / THE IDEA
Contract renewal risk predictor in practice.
Predicting whether a customer will renew a contract is a binary classification problem that requires careful labelling, feature engineering from CRM and transaction logs, and evaluation using business-aware metrics. Common failure modes include class imbalance, label noise from delayed renewals, covariate shift after pricing or policy changes, and missing or aggregated input fields.
Real-world use case · Proposed extension
A proposed application: surface contracts with a high predicted non-renewal probability for account managers to review and intervene. Any production use should keep a human reviewer in control and treat model outputs as decision support rather than automated contract actions.
02 / THE BUILD
How to build a contract renewal risk predictor project
step by step.
Build a baseline
Train a simple pipeline (scaling, tree-based classifier) to establish a reproducible baseline and log feature importances.
Evaluate with business metrics
Measure AUC, precision/recall at operational thresholds, calibration and a cost-aware confusion matrix that reflects intervention costs.
Deploy with monitoring
Expose probabilities via a small API, track calibration and data drift, and require human sign-off for automated interventions.
03 / SOURCE SNAPSHOT
What the contract renewal risk predictor starter actually does.
The supplied starter generates synthetic classification data with sklearn.datasets.make_classification, builds a Pipeline with StandardScaler and RandomForestClassifier, fits the model and prints a sklearn classification_report on a held-out test split. It does not use contract or CRM data and does not implement cost-sensitive evaluation or calibration.
SOURCE EXCERPTPython
model = Pipeline(
steps=[
("scale", StandardScaler()),
("classifier", RandomForestClassifier(n_estimators=250, random_state=RANDOM_STATE)),
]
)
model.fit(X_train, y_train)
predictions = model.predict(X_test)Important limitation
The starter uses randomly generated features from make_classification rather than real contract or CRM fields, so reported metrics do not reflect contract-renewal performance. The code also omits cost-aware evaluation, probability calibration, and any domain-specific feature engineering or temporal splitting.
04 / BEYOND THE CLASSROOM
Where a contract renewal risk predictor project could go next.
PROPOSED BUSINESS CONCEPT
Contract renewal pilot concept
A proposed extension: replace the synthetic data with a CRM export, derive time-windowed features (usage, billing, support events), train a calibrated, cost-sensitive classifier, and add explainability so account managers can act on model outputs.
WHO IT COULD SERVESales and customer-success teams
FIRST MVPA small API that returns a renewal probability and top feature contributions for a contract, plus a dashboard listing flagged contracts for human review.
KEY RISKFalse negatives may miss customers needing intervention and false positives may waste account-manager time; a human-in-the-loop must review flagged contracts before any automated action.
This is a potential application pathway, not a delivered product. The supplied starter does not implement this service or validate it with real users.
05 / WHAT YOU TAKE AWAY
What a contract renewal risk predictor project puts on your resume.
- Prepare CRM and transaction features, define a clear renewal label window and handle missing values.
- Train a reproducible baseline pipeline using scaling and a tree-based classifier and inspect feature importances.
- Evaluate models with AUC, thresholded precision/recall, calibration plots and a cost-aware confusion matrix.
- Plan deployment with an API, calibration checks and monitoring for data drift and label-delay effects.
Want to build this with mentors and a lab?
AI Engineer Course Networkers Home runs this topic inside its AI Engineer Course, with placement support and hands-on labs.
06 / QUESTIONS STUDENTS ASK
Contract Renewal Risk Predictor project FAQs.
- Is this suitable as a final year project for CSE or engineering students?
- Yes — it’s an advanced binary classification practice that suits final year students, involving data preprocessing, feature engineering, model selection and business-aware evaluation; the starter is a partial review, not a full solution.
- What skills and datasets will I need to complete it?
- You should know Python, pandas, scikit-learn, handling class imbalance, and feature engineering from CRM/transaction logs; realistic contract or CRM-like datasets are needed to get meaningful results.
- How should I evaluate progress and how long will it take?
- Evaluate using precision/recall, ROC-AUC and business metrics (e.g. expected retention uplift); with a real dataset expect several weeks for cleaning and feature work and additional weeks for modelling and validation.