@7.lollly: الحمد لله #مكة #fyp #foryou #viral #الحرم_المكي

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Friday 14 August 2026 05:53:37 GMT

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N :

وش افضل وقت مافيه زحمه الصباح ولا قبل الظهر

2026-08-20 17:34:34

4

𝓢𝓪𝓻𝓸𝓷𝓪🎀 :

ماشاءالله كنت هناك بعد الفجر 🥺

2026-08-14 18:28:32

3

🤍 :

الحمدلله على كل حال

2026-08-18 14:38:05

5

SK..@🇬.🇩🇪.🌒.🇬🇧 :

“Life is a beautiful journey, and traveling to new places teaches us many wonderful lessons. But visiting the holy places and performing Umrah is a truly special blessing that brings peace to the heart and soul. May Allah accept your prayers, bless your journey, and fill your life with happiness, peace, and countless beautiful memories. May every step you take bring you closer to your dreams and to Allah. Wishing you a safe, blessed, and unforgettable journey. With best wishes and respect, Hamid Momand. 🤲❤️✨”😍😍😍

2026-09-11 15:40:05

0

حجز فنادق مكة ووجهات سياحية :

ربى يرزق كل مشتاق 🤍

2026-08-19 12:15:37

1

تيم وصيفات ريفا' :

حبيت

2026-08-15 02:13:23

2

فيصل العنزي| 🇸🇦 :

اي والله الف الحمدالله يارب لك الحمد والشكر

2026-08-14 08:41:25

3

s_aro_ :

2026-08-17 06:24:17

2

𝔅𝔞𝔫𝔞𝔫🤍 📸 :

الحمدلله

2026-10-03 15:04:58

0

ابن الجنوب ❣️❣️❣️ :

مكه راحه نفسيه

2026-08-24 22:10:17

0

شمريه :

الذ قهوه شربته امس شربته ادمنت عليه ياليت يفتحون بحايل

2026-08-22 07:05:55

2

. :

النظاره من وين؟

2026-09-17 08:25:16

0

ÆWÆÎŠ AHMÂĐ :

2026-08-17 15:19:43

0

حموود❣️ :

الحمدلله ❣️

2026-08-20 14:15:41

1

مكة المكرمة 🕋🎥-🫆 :

2026-09-08 19:15:06

0

ابن الجنوب ❣️❣️❣️ :

الله يتقبل

2026-08-24 22:09:52

0

إسماعيل🕋 :

🤍

2026-09-14 03:55:37

0

WAQAR KHAN.🇵🇰❤️🇸🇦 :

🥰🥰🥰

2026-10-13 18:27:28

0

shoaib nawaz :

🥰🥰🥰

2026-09-09 15:58:00

0

Fahad Fadi :

❤️

2026-08-15 05:33:16

0

شـوّق🍃. :

اللهم صل وسلم وبارك على نبينا محمد

2026-09-10 20:56:23

0

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Lý Sơn đẹp lắm í

Lý Sơn đẹp lắm í

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Fraud detection is not just about building a Machine Learning model. Before training a model, we need to understand the data, identify patterns, and discover what makes fraudulent transactions different from legitimate ones. That process begins with EDA 🚀 🎯 PROJECT OBJECTIVE The goal is to explore credit card transaction data and identify patterns related to: 💳 Transaction Amount ⏱️ Transaction Time 📊 Transaction Frequency 🔍 Fraud vs Legitimate Transactions 🔄 EDA WORKFLOW 1️⃣ UNDERSTAND THE DATA 📂 Explore: 📏 Dataset Shape 📊 Data Types ❓ Missing Values 🔁 Duplicate Transactions 🎯 Target Variable Distribution 2️⃣ ANALYZE FRAUD DISTRIBUTION ⚖️ Compare: ✅ Legitimate Transactions 🚨 Fraudulent Transactions 💡 Fraud datasets are often highly imbalanced, meaning fraudulent transactions may represent only a very small percentage of all transactions. 3️⃣ ANALYZE TRANSACTION AMOUNTS 💰 Investigate: 📈 Average Transaction Amount 📊 Fraud Amount Distribution 🔍 Outliers ⚖️ Fraud vs Legitimate Amounts 4️⃣ EXPLORE TRANSACTION TIME ⏱️ Look for patterns such as: 🌙 Unusual Transaction Hours 📅 Time-Based Trends ⏰ Peak Fraud Periods 🔄 Transaction Frequency 5️⃣ FEATURE CORRELATION 🔗 Analyze relationships between variables using: 📊 Correlation Matrix 🔥 Heatmaps 📈 Distribution Plots 6️⃣ DETECT OUTLIERS 🚨 Identify unusual transactions using: 📦 Box Plots 📊 Histograms 📈 Distribution Analysis ⚠️ Statistical Methods 7️⃣ COMPARE FRAUD VS LEGITIMATE TRANSACTIONS 🔍 Ask important questions: ❓ Are fraudulent transactions larger? ❓ Do fraudsters transact at unusual times? ❓ Are certain features associated with fraud? ❓ Are there unusual patterns in transaction behaviour? 📊 VISUALIZATIONS 📈 Transaction Amount Distribution 📊 Fraud vs Legitimate Count 📦 Box Plot Analysis 🔥 Correlation Heatmap 📉 Feature Distributions ⏱️ Time-Based Analysis 🛠️ TOOLS USED 🐍 Python 🐼 Pandas 🔢 NumPy 📊 Matplotlib ✨ Seaborn 📓 Jupyter Notebook 🚀 NEXT STEP: MACHINE LEARNING After EDA, we can build models such as: 🌳 Random Forest ⚡ XGBoost 🎯 Logistic Regression 🧠 Neural Networks Then evaluate them using: 🎯 Precision 📢 Recall ⚖️ F1-Score 📊 ROC-AUC 💡 In fraud detection, accuracy alone can be misleading. A model that predicts every transaction as legitimate may achieve high accuracy while detecting zero fraud. The real challenge is finding fraudulent transactions while minimizing false alarms. Raw Data → EDA → Feature Engineering → ML Model → Fraud Detection 💳🤖 #FraudDetection #CreditCardFraud #DataScience #EDA #ExploratoryDataAnalysis

Fraud detection is not just about building a Machine Learning model. Before training a model, we need to understand the data, identify patterns, and discover what makes fraudulent transactions different from legitimate ones. That process begins with EDA 🚀 🎯 PROJECT OBJECTIVE The goal is to explore credit card transaction data and identify patterns related to: 💳 Transaction Amount ⏱️ Transaction Time 📊 Transaction Frequency 🔍 Fraud vs Legitimate Transactions 🔄 EDA WORKFLOW 1️⃣ UNDERSTAND THE DATA 📂 Explore: 📏 Dataset Shape 📊 Data Types ❓ Missing Values 🔁 Duplicate Transactions 🎯 Target Variable Distribution 2️⃣ ANALYZE FRAUD DISTRIBUTION ⚖️ Compare: ✅ Legitimate Transactions 🚨 Fraudulent Transactions 💡 Fraud datasets are often highly imbalanced, meaning fraudulent transactions may represent only a very small percentage of all transactions. 3️⃣ ANALYZE TRANSACTION AMOUNTS 💰 Investigate: 📈 Average Transaction Amount 📊 Fraud Amount Distribution 🔍 Outliers ⚖️ Fraud vs Legitimate Amounts 4️⃣ EXPLORE TRANSACTION TIME ⏱️ Look for patterns such as: 🌙 Unusual Transaction Hours 📅 Time-Based Trends ⏰ Peak Fraud Periods 🔄 Transaction Frequency 5️⃣ FEATURE CORRELATION 🔗 Analyze relationships between variables using: 📊 Correlation Matrix 🔥 Heatmaps 📈 Distribution Plots 6️⃣ DETECT OUTLIERS 🚨 Identify unusual transactions using: 📦 Box Plots 📊 Histograms 📈 Distribution Analysis ⚠️ Statistical Methods 7️⃣ COMPARE FRAUD VS LEGITIMATE TRANSACTIONS 🔍 Ask important questions: ❓ Are fraudulent transactions larger? ❓ Do fraudsters transact at unusual times? ❓ Are certain features associated with fraud? ❓ Are there unusual patterns in transaction behaviour? 📊 VISUALIZATIONS 📈 Transaction Amount Distribution 📊 Fraud vs Legitimate Count 📦 Box Plot Analysis 🔥 Correlation Heatmap 📉 Feature Distributions ⏱️ Time-Based Analysis 🛠️ TOOLS USED 🐍 Python 🐼 Pandas 🔢 NumPy 📊 Matplotlib ✨ Seaborn 📓 Jupyter Notebook 🚀 NEXT STEP: MACHINE LEARNING After EDA, we can build models such as: 🌳 Random Forest ⚡ XGBoost 🎯 Logistic Regression 🧠 Neural Networks Then evaluate them using: 🎯 Precision 📢 Recall ⚖️ F1-Score 📊 ROC-AUC 💡 In fraud detection, accuracy alone can be misleading. A model that predicts every transaction as legitimate may achieve high accuracy while detecting zero fraud. The real challenge is finding fraudulent transactions while minimizing false alarms. Raw Data → EDA → Feature Engineering → ML Model → Fraud Detection 💳🤖 #FraudDetection #CreditCardFraud #DataScience #EDA #ExploratoryDataAnalysis

1 Hour of Oddly Satisfying Slime ASMR Perfect for Relaxation & Sleep

1 Hour of Oddly Satisfying Slime ASMR Perfect for Relaxation & Sleep

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