📌 Project Overview: The Customer Transaction Prediction project aims to predict whether a customer will make a future transaction based on their past behavior and profile attributes. Using machine learning techniques, this project analyzes historical transaction data, preprocesses it, builds predictive models, and selects the best-performing model for deployment.
📂 Project Workflow
- Data Preprocessing & Analysis
Loaded dataset and explored structure.
Handled missing values and outliers.
Encoded categorical variables and scaled numerical features.
Performed Exploratory Data Analysis (EDA) to understand feature relationships.
- Model Development & Training
Split data into training and testing sets.
Implemented multiple ML algorithms: Logistic Regression, Random Forest, and XGBoost.
Applied hyperparameter tuning using GridSearchCV/RandomizedSearchCV.
- Model Evaluation & Selection:Evaluated models using metrics: Accuracy, Precision, Recall, F1-score, ROC-AUC.
Selected the best-performing model for final predictions.
Saved the trained model for future use.
🛠 Technologies Used: Python – Programming language.
Pandas, NumPy – Data manipulation.
Matplotlib, Seaborn – Data visualization.
Scikit-learn – Model building & evaluation.
XGBoost – Gradient boosting algorithm for prediction.