Task2

Import libraries and load data import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns from sklearn.model_selection import train_test_split from sklearn.preprocessing import LabelEncoder from sklearn.linear_model import LogisticRegression from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import accuracy_score, confusion_matrix, classification_report

Load the dataset

df = pd.read_csv('loan_prediction.csv')

Display the first few rows of the dataset

print(df.head())

Check for missing values

print(df.isnull().sum())

Handle missing data appropriately (e.g., fill with mean, median, or mode)

df['LoanAmount'].fillna(df['LoanAmount'].mean(), inplace=True) df['Credit_History'].fillna(df['Credit_History'].mode()[0], inplace=True)

Drop rows with missing values in other columns if any

df.dropna(inplace=True)

Visualize loan amount distribution

plt.figure(figsize=(8, 6)) sns.histplot(df['LoanAmount'], bins=20, kde=True) plt.title('Loan Amount Distribution') plt.show()

Visualize education

plt.figure(figsize=(6, 4)) sns.countplot(x='Education', data=df) plt.title('Education Level') plt.show()

Visualize income

plt.figure(figsize=(8, 6)) sns.histplot(df['ApplicantIncome'], bins=20, kde=True) plt.title('Applicant Income Distribution') plt.show()

Encode categorical variables

le = LabelEncoder() df['Gender'] = le.fit_transform(df['Gender']) df['Married'] = le.fit_transform(df['Married']) df['Education'] = le.fit_transform(df['Education']) df['Self_Employed'] = le.fit_transform(df['Self_Employed']) df['Property_Area'] = le.fit_transform(df['Property_Area'])

Define features and target

X = df.drop(['Loan_Status'], axis=1) y = df['Loan_Status'].map({'Y': 1, 'N': 0})

Split data into training and testing sets

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

Train a logistic regression model

model = LogisticRegression() model.fit(X_train, y_train)

Train a decision tree classifier

model = DecisionTreeClassifier()

model.fit(X_train, y_train)

Make predictions

y_pred = model.predict(X_test)

Evaluate the model

accuracy = accuracy_score(y_test, y_pred) print("Model Accuracy:", accuracy)

Confusion matrix

cm = confusion_matrix(y_test, y_pred) print("Confusion Matrix:") print(cm)

Classification report

print("Classification Report:") print(classification_report(y_test, y_pred))