Personal Injury Case Outcome Prediction with Machine Learning

Introduction

Predicting the outcome of personal injury cases is a crucial task for law firms aiming to optimize their case intake process and improve their win rates. By leveraging machine learning (ML), law firms can forecast whether a case is likely to be settled, won, lost, or dismissed based on data such as accident type, injury severity, client demographics, and more. This article presents a comprehensive, step-by-step guide to building an end-to-end machine learning project that predicts personal injury case outcomes. We will explore data from the US Accidents dataset, apply advanced data science techniques, train machine learning models, and deploy the final model using Streamlit for interactive web-based use.

This project is highly beneficial for data science learners, ML students, and AI enthusiasts eager to apply predictive modeling in legal tech. It will also provide valuable insights for data analysts working in law firms to automate lead qualification and estimate potential legal outcomes.

Understanding the Problem and Dataset

The objective is to create a predictive model for personal injury case outcomes—categorized as Settled, Won, Lost, or Dismissed. The key challenges include:

Handling diverse data features like accident type, injury severity, client age, and other case details.
Addressing class imbalance, as some case outcomes may be rare.
Ensuring model interpretability for legal professionals.

Dataset: We use the “US Accidents” dataset, a rich source of real-world accident data including location, time, weather conditions, and severity indicators. This dataset will be supplemented with synthesized outcome labels for demonstration.

👉 Download the dataset here: Dataset

Exploratory Data Analysis (EDA)

Exploratory Data Analysis helps to understand data distribution, identify missing values, and uncover relationships between features and outcomes.

Code Section:

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Load dataset
data = pd.read_csv('US_Accidents_Dec21_updated.csv')

# Preview data
print(data.head())

# Check for missing values
print(data.isnull().sum())

# Visualize distribution of injury severity
sns.countplot(x='Severity', data=data)
plt.title('Distribution of Injury Severity')
plt.show()

# Analyze accident types (using 'Description' or 'Side' as proxy)
sns.countplot(y='Side', data=data)
plt.title('Accident Types by Side of Road')
plt.show()

Explanation:

We preview the top rows after loading the dataset.
We check missing values to decide imputation or removal strategies.
To comprehend class proportions, visualize the injury severity distribution.
Inspect accident types through a proxy feature, helping us identify patterns.

Data Preprocessing and Feature Engineering

Preprocessing involves cleaning data, encoding categorical variables, and creating new features for better model performance.

Code Section:

from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split

# Drop irrelevant columns and handle missing data
data = data.drop(['Start_Time', 'End_Time', 'Description'], axis=1)
data = data.dropna(subset=['Severity', 'Side', 'State'])

# Encode categorical variables
label_encoders = {}
categorical_cols = ['Side', 'State', 'City']

for col in categorical_cols:
    le = LabelEncoder()
    data[col] = le.fit_transform(data[col])
    label_encoders[col] = le

# Creating a target variable (mock labels for demonstration)
import numpy as np
np.random.seed(42)
data['Outcome'] = np.random.choice(['Settled', 'Won', 'Lost', 'Dismissed'], size=len(data))

# Encode target variable
le_outcome = LabelEncoder()
data['Outcome'] = le_outcome.fit_transform(data['Outcome'])

# Split data
X = data.drop('Outcome', axis=1)
y = data['Outcome']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

Explanation:

Remove columns not useful for prediction or with high missing data.
Encode categorical columns into numerical formats using LabelEncoder.
Generate mock outcome labels for the example; in real cases, labeled data is essential.
To verify the performance of the model, divide the data into training and testing sets.

Model Selection and Training

We choose a Random Forest Classifier due to its robustness, interpretability, and ability to handle feature heterogeneity.

Code Section:

from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, confusion_matrix

# Initialize and train the model
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

# Predictions
y_pred = model.predict(X_test)

# Evaluate
print(classification_report(y_test, y_pred))
print(confusion_matrix(y_test, y_pred))

Explanation:

Random Forest is trained on the preprocessed data.
Predictions on the test set provide insight into accuracy, precision, recall, and F1-score.
Confusion matrix visualizes classification errors across outcome classes.

Model Evaluation

Model evaluation uses metrics including accuracy, precision, recall, F1-score, and confusion matrices to ensure reliable predictions.

Key points:

Check for class imbalance effects.
Validate if the model generalizes well on unseen data.
Consider cross-validation for more reliable estimates.

Deploying the Model with Streamlit

Streamlit allows quick deployment of machine learning models as interactive web apps.

Code Section:

import streamlit as st
import numpy as np

# Load the trained model and label encoders (in production, use joblib or pickle)

def predict_outcome(input_data):
    df = pd.DataFrame([input_data])
    for col, le in label_encoders.items():
        df[col] = le.transform(df[col])
    prediction = model.predict(df)
    outcome = le_outcome.inverse_transform(prediction)[0]
    return outcome

st.title("Personal Injury Case Outcome Prediction")

# Input fields
side = st.selectbox('Side of Road', list(label_encoders['Side'].classes_))
state = st.selectbox('State', list(label_encoders['State'].classes_))
city = st.selectbox('City', list(label_encoders['City'].classes_))
severity = st.slider('Injury Severity (1-4)', 1, 4, 1)

input_data = {'Side': side, 'State': state, 'City': city, 'Severity': severity}

if st.button('Predict Outcome'):
    result = predict_outcome(input_data)
    st.success(f'The predicted case outcome is: {result}')

Explanation:

Streamlit interface collects user input for relevant features.
Inputs are encoded similarly to training data.
Model predicts the outcome and displays it interactively.
This deployment helps law firms qualify leads and estimate case potential quickly.

Conclusion

Building an end-to-end machine learning system for Personal Injury Case Outcome Prediction offers transformative value to law firms. Through thorough data analysis, preprocessing, model training, and deployment via Streamlit, legal teams can make data-driven decisions, prioritize cases, and enhance client satisfaction.

Personal Injury Case Outcome Prediction systems enable law firms to automate lead qualification, reduce operational costs, and improve win rates. By applying this framework, data science learners and AI practitioners can develop impactful, scalable solutions that bring tangible value to the legal industry.

👉 Interested in building real-world projects like this? Apply now to the BiStartX Internship and take your skills in Personal Injury Case Outcome Prediction, legal tech, and machine learning to the next level!

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