You can run the code in Pycharm and vscode.
I divided the task into following things:
Importing libraries:
pandas: Used for data manipulation and analysis.
numpy: Provides support for large arrays and matrices.
matplotlib.pyplot: Used for plotting and visualization.
sklearn.model_selection: Includes tools for splitting the dataset and performing grid search.
sklearn.pipeline: Helps in creating a machine learning pipeline.
sklearn.compose: Allows combining different preprocessing steps.
sklearn.preprocessing: Contains various preprocessing utilities.
sklearn.linear_model: Provides regression algorithms like Ridge regression.
sklearn.metrics: Offers metrics for model evaluation.
Faker: Generates fake data.
Initialize Faker and Data Generation:
Faker is initialized to create realistic synthetic data.
np.random.seed(0) ensures reproducibility of random numbers.
fake_data dictionary contains:
income: Normally distributed incomes with mean 50000 and standard deviation 15000.
age: Random integers between 20 and 70.
credit_history: Random integers between 1 and 10.
credit_score: Random integers between 300 and 850.
Data Conversion:
The generated data is converted to a pandas DataFrame for easier manipulation.
Splitting Data:
X contains the features (income, age, credit_history).
y contains the target variable (credit_score).
Pipline Preprocessing:
ColumnTransformer applies transformations to specified columns.
StandardScaler standardizes features by removing the mean and scaling to unit variance.
Pipline Regression:
Pipeline chains preprocessing and model fitting steps.
Ridge regression is used as the model.
HyperParameter Tuning:
GridSearchCV performs hyperparameter tuning using cross-validation.
parameters dictionary specifies the range of alpha values for Ridge regression.
Train-Test Split and Prediction:
train_test_split splits the data into training and testing sets (80% train, 20% test).
Model Evaluation:
r2_score and mean_squared_error evaluate the model's performance.
The best hyperparameters are printed.
Prediction Function:
predict_credit_score: Function to predict credit scores using the trained model.
A sample prediction is made using specified income, age, and credit_history.
Visualization:
plt.scatter creates a scatter plot to visualize the relationship between actual and predicted credit scores.
The output looks like this:
