File size: 2,703 Bytes
e9ee222 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 | import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, classification_report
import joblib
import os
# --- Configuration ---
# Ensure your Excel file is in the project root and named this, or change the path.
DATASET_PATH = 'symptoms_dataset.xlsx'
MODEL_SAVE_PATH = 'symptom_model.joblib'
def train_symptom_model():
"""
Loads symptom data from an Excel file, trains a Logistic Regression model,
and saves it to disk.
"""
# 1. Load the dataset
try:
df = pd.read_excel(DATASET_PATH)
print(f"Dataset '{DATASET_PATH}' loaded successfully. Shape: {df.shape}")
except FileNotFoundError:
print(f"Error: The file '{DATASET_PATH}' was not found. Please create it and add your data.")
return
# 2. Define Features (X) and Target (y)
# These are the columns the model will use to learn.
features = ['tremor', 'stiffness', 'walking_issue']
# This is the column the model will try to predict.
target = 'label'
# Validate that all required columns exist in the Excel file
required_columns = features + [target]
if not all(col in df.columns for col in required_columns):
print(f"Error: Your Excel file is missing one or more required columns.")
print(f"Please ensure it contains: {required_columns}")
return
X = df[features]
y = df[target]
# 3. Split data into training and testing sets
# We use 'stratify=y' to ensure both train and test sets have a similar proportion of 0s and 1s.
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)
print(f"Data split into {len(X_train)} training samples and {len(X_test)} testing samples.")
# 4. Initialize and Train the Model
print("\nTraining Logistic Regression model...")
# We use class_weight='balanced' to handle cases where there might be more 0s than 1s or vice-versa.
model = LogisticRegression(random_state=42, class_weight='balanced')
model.fit(X_train, y_train)
print("Model training complete.")
# 5. Evaluate the Model (optional but good practice)
print("\nEvaluating model performance...")
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print(f"Model Accuracy on Test Set: {accuracy:.4f}")
print("\nClassification Report:")
print(classification_report(y_test, y_pred))
# 6. Save the Trained Model
joblib.dump(model, MODEL_SAVE_PATH)
print(f"\nSymptom model successfully saved to: {MODEL_SAVE_PATH}")
if __name__ == '__main__':
train_symptom_model() |