Predicting_Purchase_Behavior_Based-on-Salary_and_Age

Project Overview: The goal of this project is to develop a machine learning model that predicts whether a person will make a purchase based on their age and salary. This binary classification problem will help businesses understand customer behavior and tailor their marketing strategies accordingly.

Objectives: To analyze the relationship between age, salary, and purchasing behavior. To build a predictive model that accurately classifies whether a person will make a purchase. To evaluate the performance of the model using appropriate metrics.

Data Description: The dataset used in this project contains the following columns:

Age: The age of the individual. EstimatedSalary: The estimated annual salary of the individual. Purchased: A binary variable indicating whether the individual made a purchase (1) or not (0).

Methodology:

1. Data Collection: Gather data that includes the age, estimated salary, and purchase status of individuals. Data Preprocessing:
2. Handle missing values. Normalize or standardize the features if necessary.
3. Exploratory Data Analysis (EDA): Visualize the data using scatter plots, histograms, and box plots to understand the distribution and relationships between features.
4. Model Selection: Choose a suitable model for binary classification. Logistic regression will be used as the baseline model.
5. Model Training: Split the data into training and testing sets. Train the logistic regression model on the training set.
6. Model Evaluation: Evaluate the model’s performance using metrics such as accuracy, precision, recall, and the ROC-AUC score.
7. Prediction: Use the trained model to make predictions on new data.

Expected Outcomes: A trained machine learning model that can predict purchase behavior based on age and salary. Insights into how age and salary influence purchasing decisions. Visualizations that illustrate the relationships between features and the target variable.

Conclusion: This project will provide valuable insights into customer behavior and demonstrate the application of machine learning techniques in predicting binary outcomes. The findings can be used to enhance marketing strategies and improve customer targeting.