Requirements.txt(Model,library & serialization files )

**Python Libraries:**
pandas: For data manipulation and analysis.

import pandas as pd
numpy: For numerical operations and array handling.

import numpy as np
matplotlib: For data visualization.

import matplotlib.pyplot as plt
seaborn: For statistical data visualization.

import seaborn as sns
scikit-learn (sklearn): For machine learning tasks.

from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, confusion_matrix
from sklearn.decomposition import PCA
imbalanced-learn (imblearn): For handling imbalanced datasets.

from imblearn.over_sampling import SMOTE, ADASYN
from imblearn.under_sampling import RandomUnderSampler
xgboost: For using the XGBoost model.

import xgboost as xgb
pickle: For saving and loading the trained model.

import pickle
streamlit: For deploying the model as an interactive web application.

import streamlit as st
Additional Libraries (If Applicable):
warnings: For controlling the warning messages during execution.

import warnings
warnings.filterwarnings('ignore')
time: For tracking time, if you measured model training time.

import time

Model used in this proeject.
**Logistic Regression**
**XGB model**
**Decision Tree Classifier**


pandas==1.5.3
numpy==1.23.5
scikit-learn==1.5.0
imblearn==0.0
smote==0.1.1
xgboost==1.7.3
flask==2.2.2
streamlit==1.15.2
pickle