This project aims to predict potential customers for a given business using machine learning techniques. The dataset is processed to identify patterns and predict which customers are likely to make a purchase.
The goal of this project is to build a predictive model that helps a business target potential customers effectively. By analyzing customer data, the model predicts which individuals are most likely to convert.
The dataset used for this project includes various features such as customer demographics, purchase history, and online activity. It has been cleaned and preprocessed to remove missing values and outliers.
- Python
- Pandas, NumPy (Data Wrangling)
- Scikit-learn (Modeling)
- Matplotlib, Seaborn (Visualization)
- Jupyter Notebook
- Random Forest
- DecisionTree
- LogisticRegression
- GridSearchCV
The predictive model achieved a high accuracy in identifying potential customers, making it a valuable tool for targeted marketing campaigns. Key performance metrics include:
- Accuracy: 85%
- Precision: 80%
- Recall: 78%
Claudia Martinez - Data Scientist