Customer Purchase Prediction

This project aims to predict whether a customer will make a purchase based on various features using several Machine Learning (Random Forest, SVM, Logistic Regression) and Deep Learning (Artificial Neural Network) models. The dataset is preprocessed using StandardScaler. Additionally, a Streamlit app has been created to allow users to interactively predict customer purchases.

Table of Contents

• Introduction
• Dataset
• Features
• Preprocessing
• Models
• Evaluation
• Streamlit App
• Usage
• Dependencies

Introduction

The goal of this project is to build predictive models that classify whether a customer will make a purchase. Four different models are used: Random Forest, Support Vector Machine (SVM), Logistic Regression, and an Artificial Neural Network (ANN). A Streamlit app is also developed to facilitate user interaction with the predictive models.

Dataset

The dataset includes the following features:

• Age: Customer's age
• Gender: Customer's gender (0: Male, 1: Female)
• AnnualIncome: Annual income of the customer in dollars
• NumberOfPurchases: Total number of purchases made by the customer
• ProductCategory: Category of the purchased product (0: Electronics, 1: Clothing, 2: Home Goods, 3: Beauty, 4: Sports)
• TimeSpentOnWebsite: Time spent by the customer on the website in minutes
• LoyaltyProgram: Whether the customer is a member of the loyalty program (0: No, 1: Yes)
• DiscountsAvailed: Number of discounts availed by the customer (range: 0-5)
• PurchaseStatus: Likelihood of the customer making a purchase (0: No, 1: Yes) - Target Variable

Features

• Age
• Gender
• AnnualIncome
• NumberOfPurchases
• ProductCategory
• TimeSpentOnWebsite
• LoyaltyProgram
• DiscountsAvailed
• PurchaseStatus (Target Variable)

Preprocessing

The dataset is preprocessed using StandardScaler to ensure that all features have a mean of 0 and a standard deviation of 1. This helps in improving the performance and training stability of the models.

Models

Four models are built to predict the purchase status:

1. Random Forest Classifier: A robust and interpretable machine learning model.
2. Support Vector Machine (SVM): A powerful classifier for high-dimensional spaces.
3. Logistic Regression: A simple yet effective linear model.
4. Artificial Neural Network (ANN): A deep learning model capable of capturing complex patterns in the data.

Evaluation

The models are evaluated based on their accuracy on the testing dataset. The evaluation metrics include:

• Accuracy
• Precision
• Recall
• F1 Score

Streamlit App

A Streamlit app has been created to allow users to interactively predict whether a customer will make a purchase based on the input features. The app uses the trained models to make predictions and displays the results to the user.

Running the Streamlit App

1. Ensure you have Streamlit installed:

   pip install streamlit