@cutieehina: bof season 2 ending was gorgeous 🥹🥹 hinata and hanbon edit coming soon as well as the rest of the group 🤔👌🏽 #fyp #edit #hinata #hinatahyuga #transitionedit

˖ 𑣲 lynn 🧣🪻†

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Saturday 26 September 2026 19:40:09 GMT

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lolii ˚ʚ♡ɞ˚ :

I LOVE HINATA THIS IS SO CUTEEE

2026-09-27 16:49:23

1

✮⋆˙ 𝗵𝗶𝗻𝗮 !! :

omgee i love you

2026-09-26 19:41:22

3

𐂯 ɑvi :

THIS IS SO GORGEOUS???

2026-09-27 01:34:46

1

hina :

Ok i thought I would be first seeing it this early but I guess not.

2026-09-26 19:43:22

1

ℳ𐙚 :

SO CUTEEEE

2026-10-01 21:58:47

1

Meganos :

Cuteeee

2026-09-27 18:20:04

1

Joy <3 :

I LOVE YOUR HINATA EDITS

2026-09-28 18:07:13

1

⦁ yan 🍮🐈‍⬛ :

this is so beautiful omg

2026-09-26 20:12:29

1

ريوف النهدي 🇾🇪 :

SO CUTE

2026-09-26 20:09:34

1

愛 :

OMG THIS IS WHAT I CALL TALENT

2026-09-26 19:51:07

1

✮⋆˙ 𝗵𝗶𝗻𝗮 !! :

how does one achieve this level of talent

2026-09-26 19:44:14

3

✮⋆˙ 𝗵𝗶𝗻𝗮 !! :

cutest hinata edits ever

2026-09-26 19:41:33

3

hina :

I really freaking adore ur edit style

2026-09-26 19:43:49

1

hina :

Hinata my beautiful baby

2026-09-26 19:43:36

2

hina :

Hihihihihi

2026-09-26 19:43:30

1

⦁ yan 🍮🐈‍⬛ :

OHMYGOD HINA

2026-09-26 20:13:20

1

✮⋆˙ 𝗵𝗶𝗻𝗮 !! :

2026-09-26 19:41:41

2

愛 :

OMG BRO THIS IS SO BEAUTIFUL AND SMOOTH

2026-09-26 19:51:16

1

hina :

I like this melody 🥰🥰

2026-09-26 19:43:55

1

𝄞 :

Her and her cinnamon rolls

2026-09-26 23:23:55

1

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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

مرکز الحجامه روحانی# دارالشفاء #هلمند #لښکرګاه #واټسف0744718845 #واټسف0705584900 #فیرکفنګ

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