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"cells": [
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"source": [
"# ML Practice Series: Module 26 - End-to-End ML Project (Production Pipeline)\n",
"\n",
"This is the **FINAL MODULE** and the ultimate test of everything you've learned. You will build a complete, production-ready ML system from scratch that includes:\n",
"\n",
"### Full Production Workflow:\n",
"1. **Problem Definition & Data Collection**\n",
"2. **EDA & Statistical Analysis**\n",
"3. **Feature Engineering & Selection**\n",
"4. **Model Selection & Hyperparameter Tuning**\n",
"5. **Model Evaluation & Explainability (SHAP)**\n",
"6. **Model Persistence & Deployment**\n",
"7. **Monitoring & Documentation**\n",
"\n",
"### Dataset:\n",
"We will use the **Credit Card Fraud Detection** dataset (highly imbalanced, real-world complexity).\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Phase 1: Problem Understanding & Data Loading\n",
"\n",
"### Business Goal:\n",
"Build a model to detect fraudulent credit card transactions to minimize financial losses.\n",
"\n",
"**Success Metrics**: Precision, Recall, F1-Score (since data is imbalanced)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"from sklearn.model_selection import train_test_split, GridSearchCV\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.metrics import classification_report, confusion_matrix, roc_auc_score\n",
"import joblib\n",
"\n",
"# For this demo, we'll use a simulated dataset\n",
"# In production, replace with: pd.read_csv('creditcard.csv')\n",
"np.random.seed(42)\n",
"df = pd.DataFrame({\n",
" 'Amount': np.random.uniform(1, 5000, 1000),\n",
" 'Time': np.random.uniform(0, 172800, 1000),\n",
" 'V1': np.random.randn(1000),\n",
" 'V2': np.random.randn(1000),\n",
" 'Class': np.random.choice([0, 1], 1000, p=[0.95, 0.05])\n",
"})\n",
"\n",
"print(\"Dataset loaded!\")\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Phase 2: Exploratory Data Analysis (EDA)\n",
"\n",
"### Task 1: Check Class Imbalance\n",
"Plot the distribution of fraud vs non-fraud transactions."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# YOUR CODE HERE"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<details>\n",
"<summary><b>Click to see Solution</b></summary>\n",
"\n",
"```python\n",
"sns.countplot(x='Class', data=df)\n",
"plt.title('Fraud vs Normal Transactions')\n",
"plt.show()\n",
"print(df['Class'].value_counts())\n",
"```\n",
"</details>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Phase 3: Feature Engineering\n",
"\n",
"### Task 2: Scaling & Train-Test Split\n",
"1. Scale the `Amount` and `Time` columns\n",
"2. Split data (80/20) with stratification"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# YOUR CODE HERE"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<details>\n",
"<summary><b>Click to see Solution</b></summary>\n",
"\n",
"```python\n",
"scaler = StandardScaler()\n",
"df[['Amount', 'Time']] = scaler.fit_transform(df[['Amount', 'Time']])\n",
"\n",
"X = df.drop('Class', axis=1)\n",
"y = df['Class']\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(\n",
" X, y, test_size=0.2, stratify=y, random_state=42\n",
")\n",
"```\n",
"</details>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Phase 4: Model Training & Hyperparameter Tuning\n",
"\n",
"### Task 3: GridSearchCV\n",
"Use GridSearch to find the best `max_depth` and `n_estimators` for a Random Forest."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# YOUR CODE HERE"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<details>\n",
"<summary><b>Click to see Solution</b></summary>\n",
"\n",
"```python\n",
"param_grid = {\n",
" 'n_estimators': [50, 100],\n",
" 'max_depth': [10, 20, None]\n",
"}\n",
"\n",
"rf = RandomForestClassifier(random_state=42)\n",
"grid = GridSearchCV(rf, param_grid, cv=3, scoring='f1')\n",
"grid.fit(X_train, y_train)\n",
"\n",
"print(\"Best params:\", grid.best_params_)\n",
"best_model = grid.best_estimator_\n",
"```\n",
"</details>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Phase 5: Model Evaluation\n",
"\n",
"### Task 4: Comprehensive Metrics\n",
"Evaluate with Confusion Matrix, Classification Report, and ROC-AUC."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# YOUR CODE HERE"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<details>\n",
"<summary><b>Click to see Solution</b></summary>\n",
"\n",
"```python\n",
"y_pred = best_model.predict(X_test)\n",
"\n",
"print(classification_report(y_test, y_pred))\n",
"print(\"ROC-AUC:\", roc_auc_score(y_test, best_model.predict_proba(X_test)[:, 1]))\n",
"\n",
"sns.heatmap(confusion_matrix(y_test, y_pred), annot=True, fmt='d')\n",
"plt.show()\n",
"```\n",
"</details>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Phase 6: Model Persistence\n",
"\n",
"### Task 5: Save the Pipeline\n",
"Save the scaler and model for production deployment."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# YOUR CODE HERE"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<details>\n",
"<summary><b>Click to see Solution</b></summary>\n",
"\n",
"```python\n",
"joblib.dump(best_model, 'fraud_model.pkl')\n",
"joblib.dump(scaler, 'scaler.pkl')\n",
"print(\"Production artifacts saved!\")\n",
"```\n",
"</details>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"--- \n",
"### 🎓 CONGRATULATIONS! \n",
"You have completed the **ENTIRE 26-MODULE CURRICULUM**. \n",
"\n",
"You are now ready to:\n",
"- Build production ML systems\n",
"- Compete in Kaggle competitions\n",
"- Interview for Data Scientist roles\n",
"- Deploy models to the real world\n",
"\n",
"**Your journey has just begun!** 🚀"
]
}
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