Tahmine Dayalı Müşteri Puanlama Kodu Yaz

Generate a comprehensive code structure for a Predictive Lead Scoring model. The code should be in [PROGRAMMING LANGUAGE] and utilize the following [LIBRARIES].

**Step 1: Data Loading and Initial Exploration**
Provide code to load a sample dataset (assume CSV) and display its first 5 rows, along with basic descriptive statistics and data types. Comment on potential missing values or inconsistencies based on [DATASET COLUMNS].

**Step 2: Data Preprocessing and Feature Engineering**
Generate code for common preprocessing steps, including:
- Handling missing values (e.g., imputation for numerical, mode for categorical).
- Encoding categorical features (e.g., One-Hot Encoding).
- Scaling numerical features (e.g., StandardScaler).
- Create at least two relevant new features based on common lead scoring practices (e.g., interaction terms, recency features, or frequency features if applicable to [DATASET COLUMNS]).

**Step 3: Model Definition and Training**
Write code to define and train a [MODEL TİPİ] model. Ensure the dataset is split into training and testing sets (e.g., 80/20 split). Clearly define features (X) and the target variable (y), which is [HEDEF DEĞİŞKEN].

**Step 4: Model Evaluation**
Provide code to evaluate the trained model on the test set. Include metrics relevant for classification problems, such as accuracy, precision, recall, F1-score, and AUC-ROC. Display a classification report and a confusion matrix.

**Step 5: Predictive Scoring and Interpretation**
Generate code to use the trained model to predict lead scores (probabilities) for new, unseen data. Show how to interpret these scores (e.g., assigning a 'High', 'Medium', 'Low' lead category based on probability thresholds). Provide a basic example of how to make a single prediction.

Generate a comprehensive code structure for a Predictive Lead Scoring model. The code should be in [PROGRAMMING LANGUAGE] and utilize the following [LIBRARIES].

**Step 1: Data Loading and Initial Exploration**
Provide code to load a sample dataset (assume CSV) and display its first 5 rows, along with basic descriptive statistics and data types. Comment on potential missing values or inconsistencies based on [DATASET COLUMNS].

**Step 2: Data Preprocessing and Feature Engineering**
Generate code for common preprocessing steps, including:
- Handling missing values (e.g., imputation for numerical, mode for categorical).
- Encoding categorical features (e.g., One-Hot Encoding).
- Scaling numerical features (e.g., StandardScaler).
- Create at least two relevant new features based on common lead scoring practices (e.g., interaction terms, recency features, or frequency features if applicable to [DATASET COLUMNS]).

**Step 3: Model Definition and Training**
Write code to define and train a [MODEL TİPİ] model. Ensure the dataset is split into training and testing sets (e.g., 80/20 split). Clearly define features (X) and the target variable (y), which is [HEDEF DEĞİŞKEN].

**Step 4: Model Evaluation**
Provide code to evaluate the trained model on the test set. Include metrics relevant for classification problems, such as accuracy, precision, recall, F1-score, and AUC-ROC. Display a classification report and a confusion matrix.

**Step 5: Predictive Scoring and Interpretation**
Generate code to use the trained model to predict lead scores (probabilities) for new, unseen data. Show how to interpret these scores (e.g., assigning a 'High', 'Medium', 'Low' lead category based on probability thresholds). Provide a basic example of how to make a single prediction.