Car Accident Case Prediction Using Machine Learning

1. Introduction

In the legal industry, attorneys often struggle to determine which car accident cases are worth pursuing. This uncertainty wastes time, resources, and energy. What if we could automate this decision-making using machine learning?

This article introduces a machine learning-based solution that predicts the viability of car accident cases using both structured data (e.g., accident severity, vehicle damage) and unstructured data (e.g., accident descriptions). By integrating NLP techniques and deploying via Streamlit, we can build a fully functional tool to assist legal professionals in qualifying leads and prioritizing high-value cases.

2. Why Predicting Case Viability Matters

Legal firms handle thousands of leads, but not every case is legally or financially viable. Predicting which cases have a higher chance of success allows law firms to:

Save operational costs
Improve conversion rates
Focus on high-reward opportunities
Offer faster client onboarding

With AI-powered lead qualification, firms can gain a competitive advantage in the legal tech space.

3. Data Used for Prediction

Structured Data:

Accident severity
Weather conditions
Number of vehicles involved
Injuries reported
Police involvement
Property damage

Unstructured Data:

Accident descriptions
Witness statements
Police report summaries

Dataset Source:

For this project, we use a publicly available dataset with anonymized car accident records:

Car Accident Severity Data – Kaggle

This dataset contains detailed attributes on over 2 million accident records from the United States, including location, timestamp, weather, and descriptive text fields. It’s ideal for modeling accident severity and legal case viability.

4. Exploratory Data Analysis (EDA)

Before modeling, exploratory data analysis (EDA) aids in pattern recognition, anomaly detection, and insight extraction.

4.1 Data Overview

print(data.info())
print(data.describe())
print(data.head())

4.2 Missing Values

import seaborn as sns
import matplotlib.pyplot as plt

plt.figure(figsize=(10,6))
sns.heatmap(data.isnull(), cbar=False, cmap='viridis')
plt.title('Missing Values Heatmap')
plt.show()

4.3 Distribution of Target Variable

sns.countplot(x='case_viable', data=data)
plt.title('Distribution of Case Viability')
plt.xlabel('Case Viable')
plt.ylabel('Count')
plt.show()

4.4 Severity vs Case Viability

sns.boxplot(x='case_viable', y='severity', data=data)
plt.title('Severity vs Case Viability')
plt.show()

4.5 Text Length Distribution

data['text_length'] = data['accident_description'].apply(lambda x: len(str(x).split()))
sns.histplot(data['text_length'], bins=50, kde=True)
plt.title('Distribution of Accident Description Lengths')
plt.xlabel('Word Count')
plt.ylabel('Frequency')
plt.show()

5. Machine Learning Workflow

Our project follows the standard ML workflow:

Data Collection
Data Preprocessing
Feature Engineering
Text Processing (TF-IDF / Word Embeddings)
Model Training (Random Forest / Logistic Regression)
Model Evaluation (Accuracy, ROC AUC)
Deployment (Streamlit)

6. Streamlit App Deployment

We will deploy our predictive model using Streamlit. The web app will:

Accept structured inputs
Accept accident description text
Predict whether the case is viable
Provide probability/confidence score

7. Code Walkthrough

Import Libraries

import pandas as pd
import numpy as np
import streamlit as st
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics import accuracy_score, classification_report
from sklearn.pipeline import Pipeline
import joblib

Data Preprocessing

# Load sample data (synthetic or anonymized)
data = pd.read_csv("car_accident_cases.csv")
data.dropna(inplace=True)
data['case_viable'] = data['case_viable'].map({'yes': 1, 'no': 0})

Feature Engineering

X_structured = data[['severity', 'injuries', 'vehicles_involved']]
X_text = data['accident_description']
y = data['case_viable']

Text Processing and Modeling Pipeline

X_train_text, X_test_text, y_train, y_test = train_test_split(X_text, y, test_size=0.2, random_state=42)

pipeline = Pipeline([
    ('tfidf', TfidfVectorizer(stop_words='english', max_features=500)),
    ('clf', RandomForestClassifier(n_estimators=100, random_state=42))
])

pipeline.fit(X_train_text, y_train)
y_pred = pipeline.predict(X_test_text)
print(classification_report(y_test, y_pred))

# Save model
joblib.dump(pipeline, 'case_viability_model.pkl')

Streamlit App

# streamlit_app.py
st.title("Car Accident Case Viability Predictor")

st.write("### Enter Structured Information")
severity = st.selectbox("Accident Severity", [1, 2, 3, 4, 5])
injuries = st.slider("Number of Injuries", 0, 10)
vehicles_involved = st.slider("Vehicles Involved", 1, 5)

st.write("### Enter Description")
description = st.text_area("Accident Description")

if st.button("Predict Case Viability"):
    model = joblib.load('case_viability_model.pkl')
    prediction = model.predict([description])[0]
    probability = model.predict_proba([description])[0][1]
    
    if prediction == 1:
        st.success(f"✅ Case is Viable (Confidence: {probability:.2f})")
    else:
        st.error(f"❌ Case Not Viable (Confidence: {probability:.2f})")

Run Streamlit App

streamlit run streamlit_app.py

8. Conclusion

The legal sector is changing due to AI and machine learning. By combining structured accident data with natural language processing of accident descriptions, we can accurately predict the viability of legal cases. This empowers attorneys with better decision-making tools and enhances efficiency and client satisfaction.

Such tools pave the way for a smarter, data-driven legal practice where time and resources are spent only on promising leads.

Ready to revolutionize your legal decision-making? Try building this ML model and deploy your own app today. If you’re a data science learner or legal tech enthusiast, this project is your perfect portfolio booster!

More Machine Learning Project Ideas to Sharpen Your Skills

Looking to expand your machine learning portfolio? Here are some impactful project ideas that cover a wide range of real-world applications—perfect for data science learners and AI enthusiasts:

⚖️ Personal Injury Case Outcome Prediction
Build a classification model that predicts the outcome of personal injury legal cases using historical court data and legal documents.

🚢 Titanic Dataset – Exploratory Data Analysis & Prediction
Perform in-depth exploratory data analysis (EDA) on the Titanic dataset, uncover hidden patterns, and develop predictive models to forecast survival outcomes.

🛒 Big Mart Sales Prediction Project (2025 Edition)
Use regression techniques to forecast sales across various Big Mart outlets by analyzing product features, store types, and seasonal demand.

🥔 Potato Leaf Disease Detection Using Deep Learning
Apply computer vision techniques to classify diseases in potato leaves. Utilize CNN-based models for accurate plant health diagnostics.

✋ Hand Gesture Recognition with Deep Learning
Design a deep learning system that recognizes hand gestures in real-time using Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).

💳 Credit Card Fraud Detection System
Detect fraudulent transactions using machine learning and anomaly detection techniques. Focus on precision and real-time prediction to reduce financial risk.

🛡️ Insurance Claim Severity Modeling
Forecast the severity of insurance claims by analyzing policyholder profiles, claim types, and incident data using advanced regression or XGBoost models.

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