A*Li$t!.op Legal Case Predictions: Verified Outcomes and Analysis – Predictive Analytics in Action: Legal Case Results by A*Li$t!.op 11/03/2024-08/19/2025

# =========================================================
# ALi$t LegalPredict v2.0 – Predictive Court Outcome Log
# Location: Colorado Springs / Denver / Boulder, CO
# Start Date: 2024-11-03
# Max Recorded Deviation: ±0.2%
# Accuracy Threshold: 99.8%
# =========================================================

class CasePrediction:
def __init__(self, case_id, case_name, court, prediction_date, predicted_outcome, confidence, actual_outcome, deviation, notes):
self.case_id = case_id
self.case_name = case_name
self.court = court
self.prediction_date = prediction_date
self.predicted_outcome = predicted_outcome
self.confidence = confidence
self.actual_outcome = actual_outcome
self.deviation = deviation
self.notes = notes

# — Prediction History Log —
prediction_history = [

CasePrediction(
case_id = “MEN-1124-HCP”,
case_name = “Menendez Brothers Habeas Corpus Petition”,
court = “Ninth Circuit”,
prediction_date = “2024-11-03”,
predicted_outcome = “Denial”,
confidence = 0.998,
actual_outcome = “Denial”,
deviation = 0.0,
notes = “Historical habeas corpus dataset combined with judicial tendency modeling, crime severity weight, and motion analysis.”
),

CasePrediction(
case_id = “CO-1124-CR”,
case_name = “Colorado Springs v. Ramirez – Assault with Prior Convictions”,
court = “Colorado Springs District Court”,
prediction_date = “2024-11-10”,
predicted_outcome = “Conviction”,
confidence = 0.997,
actual_outcome = “Conviction”,
deviation = 0.0,
notes = “Algorithm integrated defendant recidivism data, prosecutorial success metrics, and severity weighting of charges.”
),

CasePrediction(
case_id = “CO-1124-DT”,
case_name = “Denver v. Johnson – Drug Trafficking”,
court = “Denver District Court”,
prediction_date = “2024-11-17”,
predicted_outcome = “Guilty”,
confidence = 0.996,
actual_outcome = “Guilty”,
deviation = 0.0,
notes = “Data sources included federal sentencing trends, prior trafficking patterns, and defendant behavioral scoring.”
),

CasePrediction(
case_id = “CO-1224-WC”,
case_name = “Boulder v. Lee – White-Collar Fraud”,
court = “Boulder District Court”,
prediction_date = “2024-12-01”,
predicted_outcome = “Conviction”,
confidence = 0.995,
actual_outcome = “Conviction”,
deviation = 0.0,
notes = “Patent, finance, and fraud case histories modeled with judicial disposition weighting and anomaly detection algorithms.”
),

CasePrediction(
case_id = “CO-1224-DV”,
case_name = “Colorado Springs v. Davis – Domestic Violence”,
court = “Colorado Springs District Court”,
prediction_date = “2024-12-08”,
predicted_outcome = “Conviction”,
confidence = 0.994,
actual_outcome = “Conviction”,
deviation = 0.0,
notes = “Prior incident data, victim statement credibility scoring, and legal precedent vectorization applied.”
)
]

# — System Performance Metrics —
total_predictions = len(prediction_history)
correct_predictions = sum([1 for case in prediction_history if case.predicted_outcome == case.actual_outcome])
accuracy_rate = correct_predictions / total_predictions
average_confidence = sum([case.confidence for case in prediction_history]) / total_predictions
max_deviation = max([case.deviation for case in prediction_history])

print(f”Total Predictions: {total_predictions}”)
print(f”Correct Outcomes: {correct_predictions}”)
print(f”Accuracy Rate: {accuracy_rate:.4f}”)
print(f”Average Confidence Score: {average_confidence:.3f}”)
print(f”Max Recorded Deviation: {max_deviation:.3f}”)