Artificial Intelligence & ChatGPT Prompts

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Layers of AI

Evolution of Storage Devices📋 React ❤️ if you like this content #techinfo

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✅ Data Science Project Series: Part 1 - Loan Prediction. Project goal Predict loan approval using applicant data. Business value - Faster decisions - Lower default risk - Clear interview story Dataset Use the common Loan Prediction dataset from analytics practice platforms. Target Loan_Status Y approved N rejected Tech stack - Python - Pandas - NumPy - Matplotlib - Seaborn - Scikit-learn Step 1. Import libraries

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.metrics import accuracy_score, confusion_matrix, classification_report

Step 2. Load data

df = pd.read_csv("loan_prediction.csv")
df.head()

Step 3. Basic checks

df.shape
df.info()
df.isnull().sum()

Step 4. Data cleaning Fill missing values

df['LoanAmount'].fillna(df['LoanAmount'].median(), inplace=True)
df['Loan_Amount_Term'].fillna(df['Loan_Amount_Term'].mode()[0], inplace=True)
df['Credit_History'].fillna(df['Credit_History'].mode()[0], inplace=True)
categorical_cols = ['Gender','Married','Dependents','Self_Employed']
for col in categorical_cols:
    df[col].fillna(df[col].mode()[0], inplace=True)

Step 5. Exploratory Data Analysis Credit history vs approval

sns.countplot(x='Credit_History', hue='Loan_Status', data=df)
plt.show()
Income distribution.python
sns.histplot(df['ApplicantIncome'], kde=True)
plt.show()

Insight Applicants with credit history have far higher approval rates. Step 6. Feature engineering Create total income.

df['TotalIncome'] = df['ApplicantIncome'] + df['CoapplicantIncome']

# Log transform loan amount
df['LoanAmount_log'] = np.log(df['LoanAmount'])

Step 7. Encode categorical variables

le = LabelEncoder()
for col in df.select_dtypes(include='object').columns:
    df[col] = le.fit_transform(df[col])

Step 8. Split features and target

X = df.drop('Loan_Status', axis=1)
y = df['Loan_Status']
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.3, random_state=42
)

Step 9. Build model Logistic Regression.

model = LogisticRegression(max_iter=1000)
model.fit(X_train, y_train)

Step 10. Predictions

y_pred = model.predict(X_test)

Step 11. Evaluation

accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
confusion_matrix(y_test, y_pred)
Classification report.python
print(classification_report(y_test, y_pred))

Typical result - Accuracy around 80 percent - Strong precision for approved loans - Recall needs focus for rejected loans Step 12. Model improvement ideas - Use Random Forest - Tune hyperparameters - Handle class imbalance - Track recall for rejected cases Resume bullet example - Built loan approval prediction model using Logistic Regression - Achieved ~80 percent accuracy - Identified credit history as top approval driver Interview explanation flow - Start with bank risk problem - Explain feature impact - Justify Logistic Regression - Discuss recall vs accuracy Double Tap ♥️ For More

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🚩🚩 Ways to Use ChatGPT in Your Classroom.

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