Bank Marketing Prediction Project

A machine learning project designed to predict customer responses to a bank's telemarketing campaigns using real-world data. The workflow included:

Data Loading & Exploration: Using pandas and numpy for data manipulation and initial analysis.

Data Visualization: Applied matplotlib and seaborn to uncover insights and visualize trends.

Data Cleaning: Handled missing values and ensured data quality using techniques like fillna() and isna().

Data Encoding: Converted categorical variables using LabelEncoder and get_dummies.

Model Building: Trained models using scikit-learn with algorithms like Logistic Regression.

Model Evaluation: Assessed performance using accuracy_score, confusion_matrix, and classification_report.

Technologies Used: Python, Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn