43 lines
1.4 KiB
Python
43 lines
1.4 KiB
Python
import pandas as pd
|
|
from sklearn.model_selection import train_test_split
|
|
from sklearn.linear_model import LogisticRegression
|
|
from sklearn.preprocessing import LabelEncoder
|
|
from sklearn.metrics import accuracy_score, classification_report
|
|
import pickle
|
|
|
|
data = pd.read_excel('/Users/apple/Downloads/21092023-July_Sept.xlsx', sheet_name="op")
|
|
data = data.dropna()
|
|
print(data)
|
|
|
|
label_encoder = LabelEncoder()
|
|
categorical_cols = ['Gender', 'Caste', 'Category', 'Marital Status', 'Blood Group']
|
|
for col in categorical_cols:
|
|
data[col] = label_encoder.fit_transform(data[col])
|
|
|
|
X = data[['Age', 'Gender', 'Caste', 'Category', 'Marital Status']]
|
|
y = data['Test Result']
|
|
|
|
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
|
|
|
|
model = LogisticRegression()
|
|
model.fit(X_train, y_train)
|
|
|
|
y_pred = model.predict(X_test)
|
|
|
|
accuracy = accuracy_score(y_test, y_pred)
|
|
classification_report_result = classification_report(y_test, y_pred)
|
|
|
|
print(f"Accuracy: {accuracy}")
|
|
print("Classification Report:")
|
|
print(classification_report_result)
|
|
|
|
# with open('logistic_regression_model.pkl', 'wb') as model_file:
|
|
# pickle.dump(model, model_file)
|
|
|
|
|
|
# with open('logistic_regression_model.pkl', 'rb') as model_file:
|
|
# loaded_model = pickle.load(model_file)
|
|
|
|
# new_data = pd.DataFrame({'Age': [30], 'Gender': ['MALE'], 'Caste': ['SC'], 'Category': [''], 'Marital Status': ['Single']})
|
|
# predicted_result = loaded_model.predict(new_data)
|
|
# print(predicted_result) |