Create gbm.py- Solving issue #128

[Aditi22Bansal]

Solved issue #128
Data Loading and Basic Information: Loading the dataset and displaying basic information.
Missing Values Check: Checking for missing values in the dataset.
Class Distribution: Checking and visualizing the class distribution to identify any imbalance.
Summary Statistics: Displaying summary statistics of the dataset.
Label Encoding: Encoding categorical features using LabelEncoder.
Feature and Target Definition: Separating features and target variable.
Preprocessing: Defining preprocessing steps for numerical and categorical features.
Pipeline and Model Definition: Creating a pipeline that includes preprocessing and the Gradient Boosting model.
Train-Test Split: Splitting the data into training and testing sets.
Hyperparameter Tuning: Using GridSearchCV to find the best hyperparameters for the model.
Model Evaluation: Evaluating the model on the test set and displaying metrics.
ROC Curve: Plotting the ROC curve to evaluate the model's performance.
Feature Importance: Displaying and plotting the feature importances.
Model Saving: Saving the best model using joblib.

Attaching screenshots of the results

[github-actions]

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[Aditi22Bansal]

@sanjay-kv
Kindly review this PR and approve
Thanks