Coding Projects
Channel specialized for advanced concepts and projects to master: * Python programming * Web development * Java programming * Artificial Intelligence * Machine Learning Managed by: @love_data
Ko'proq ko'rsatish📈 Telegram kanali Coding Projects analitikasi
Coding Projects (@programming_experts) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 67 926 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 1 914-o'rinni va Hindiston mintaqasida 4 746-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 67 926 obunachiga ega bo‘ldi.
27 Oktabr, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 187 ga, so‘nggi 24 soatda esa 1 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 3.29% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.05% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 2 233 marta ko‘riladi; birinchi sutkada odatda 710 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 5 ta reaksiya keladi.
- Tematik yo‘nalishlar: Kontent |--, algorithm, array, framework, javascript kabi asosiy mavzularga jamlangan.
📝 Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“Channel specialized for advanced concepts and projects to master:
* Python programming
* Web development
* Java programming
* Artificial Intelligence
* Machine Learning
Managed by: @love_data”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 28 Oktabr, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.
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 Morelet, const, and var to declare variables.
let name = "John"; // can change later
const age = 25; // constant, can't be changed
var city = "Delhi"; // older syntax, avoid using it
▶️ Tip: Use let for variables that may change and const for fixed values.
2️⃣ Functions – Reusable Blocks of Code
function greet(user) {
return "Hello " + user;
}
console.log(greet("Alice")); // Output: Hello Alice
▶️ Use functions to avoid repeating the same code.
3️⃣ Arrays – Lists of Values
let fruits = ["apple", "banana", "mango"];
console.log(fruits[0]); // Output: apple
console.log(fruits.length); // Output: 3
▶️ Arrays are used to store multiple items in one variable.
4️⃣ Loops – Repeating Code
for (let i = 0; i < 3; i++) {
console.log("Hello");
}
let colors = ["red", "green", "blue"];
for (let color of colors) {
console.log(color);
}
▶️ Loops help you run the same code multiple times.
5️⃣ Conditions – Making Decisions
let score = 85;
if (score >= 90) {
console.log("Excellent");
} else if (score >= 70) {
console.log("Good");
} else {
console.log("Needs Improvement");
}
▶️ Use if, else if, and else to control flow based on logic.
🎯 Practice Tasks:
• Write a function to check if a number is even or odd
• Create an array of 5 names and print each using a loop
• Write a condition to check if a user is an adult (age ≥ 18)
💬 Tap ❤️ for more!