Question: enhance the following Exploratory Analysis : describe the data including the source, the collection method, and variables. Perform exploratory analysis. Also, select few key variables
enhance the following
| Exploratory Analysis: describe the data including the source, the collection method, and variables. Perform exploratory analysis. Also, select few key variables (including the target variable for supervised learning) and study their distributions using plots such as histograms, box plot, bar chart, etc. |
The dataset includes both numerical and categorical features. Numerical attributes such as 'duration', 'credit_amount', and 'age' were explored through summary statistics. Categorical features (e.g., 'checking_status', 'employment', 'housing') were encoded for model use. A correlation heatmap was created for numeric features to identify any strong multicollinearity. from sklearn. datasets import fetch_openml from sklearn. model_selection import train_test_split, GridSearchCV, StratifiedKFold from sklearn. preprocessing import OneHotEncoder, Standardscaler from sklearn. compose import ColumnTransformer from sklearn. pipeline import Pipeline from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay, roc_auc_score from sklearn. svm import SVC #This dataset classifies people described by a set of attributes as good or bad credit risks. creditg = fetch_openml (name= 'credit-g', as_frame=True) print(creditg. frame. columns) print (creditg. frame. describe()) print(creditg. details) /usr/local/lib/python3.12/dist-packages/sklearn/datasets/_openml. py:323: UserWarning: Multiple active vers. - version 1, status: active url: https://www. openml. org/search?type=data |
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