boladeE
160 lines
3.3 KiB
Plaintext
160 lines
3.3 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "2c5baf8e",
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"metadata": {},
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"source": [
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"# 📊 Exploratory Data Analysis: Fraud Detection Dataset"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "2f3e6a97",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"\n",
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"df = pd.read_csv(\"fraudTest.csv\")\n",
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"df.head()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2bcadae6",
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"metadata": {},
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"source": [
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"## 🧾 Basic Overview of the Dataset"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "820cb0e9",
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"metadata": {},
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"outputs": [],
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"source": [
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"print(\"Shape:\", df.shape)\n",
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"print(\"\\nData Types:\\n\", df.dtypes)\n",
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"print(\"\\nMissing Values:\\n\", df.isnull().sum())\n",
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"print(\"\\nDuplicate Rows:\", df.duplicated().sum())"
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]
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},
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{
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"cell_type": "markdown",
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"id": "caa22db9",
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"metadata": {},
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"source": [
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"## ⚖️ Class Balance"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7fb75259",
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"metadata": {},
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"outputs": [],
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"source": [
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"sns.countplot(data=df, x=\"is_fraud\")\n",
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"plt.title(\"Fraud vs Non-Fraud Transactions\")\n",
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"plt.show()\n",
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"\n",
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"fraud_ratio = df[\"is_fraud\"].mean()\n",
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"print(f\"Fraudulent transactions: {fraud_ratio:.4%}\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "658e9cd2",
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"metadata": {},
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"source": [
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"## 📊 Statistical Summary"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "202e2612",
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"metadata": {},
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"outputs": [],
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"source": [
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"df.describe(include='all')"
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]
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},
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{
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"cell_type": "markdown",
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"id": "12d24a95",
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"metadata": {},
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"source": [
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"## 🔗 Correlation Matrix"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3c02acf0",
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.figure(figsize=(12, 8))\n",
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"sns.heatmap(df.corr(numeric_only=True), annot=True, fmt=\".2f\", cmap=\"coolwarm\")\n",
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"plt.title(\"Feature Correlation Matrix\")\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "fce8183a",
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"metadata": {},
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"source": [
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"## 💵 Transaction Amount Distribution by Fraud"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ea72b131",
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.figure(figsize=(10, 6))\n",
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"sns.boxplot(data=df, x='is_fraud', y='amt')\n",
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"plt.yscale('log')\n",
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"plt.title(\"Transaction Amount by Fraud Status\")\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a7d7d378",
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"metadata": {},
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"source": [
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"## 🕒 Transaction Timing (Hourly)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "5f26f36f",
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"metadata": {},
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"outputs": [],
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"source": [
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"df['trans_date_trans_time'] = pd.to_datetime(df['trans_date_trans_time'])\n",
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"df['hour'] = df['trans_date_trans_time'].dt.hour\n",
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"\n",
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"plt.figure(figsize=(12, 6))\n",
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"sns.histplot(data=df, x='hour', hue='is_fraud', multiple='stack', bins=24)\n",
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"plt.title(\"Transaction Hour Distribution\")\n",
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"plt.show()"
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]
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}
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],
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"metadata": {},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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