model.py

import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import LabelEncoder, StandardScaler
from sklearn.pipeline import make_pipeline
from sklearn.metrics import accuracy_score
import joblib

data = pd.read_csv('train dataset.csv')

le = LabelEncoder()
data['Gender'] = le.fit_transform(data['Gender'])
input_cols = ['Gender', 'Age', 'openness', 'neuroticism', 'conscientiousness', 'agreeableness', 'extraversion']
output_cols = ['Personality (Class label)']

scaler = StandardScaler()
data[input_cols] = scaler.fit_transform(data[input_cols])
data.head()

X = data[input_cols]
Y = data[output_cols]

model = LogisticRegression(multi_class='multinomial', solver='newton-cg',max_iter =1000)
model.fit(X, Y)  

test_data = pd.read_csv('test dataset.csv')
test_data['Gender'] = le.fit_transform(test_data['Gender'])
test_data[input_cols] = scaler.fit_transform(test_data[input_cols])
X_test = test_data[input_cols]
Y_test = test_data['Personality (class label)']
test_data.head()

y_pred= model.predict(X_test)  

print(accuracy_score(Y_test,y_pred)*100)

joblib.dump(model, "train_model.pkl")