Purchase-Prediction-Model-for-an-Advertising-Campaign-on-a-Social-Network

Purchase Prediction Model for an Advertising Campaign on a Social Network - Own Project Capstone HarvardX 125.9

# Predictive Model Evaluation

This document provides a step-by-step analysis of a predictive model for a social network advertising campaign. The analysis involves data exploration, model training, performance evaluation, and visualization of results using R.

## Getting Started

Before we proceed, make sure to install and load the required R libraries:

```R
library(caTools)
library(class)
library(randomForest)
library(caret)
library(ggplot2)

Data Retrieval

We begin by retrieving the dataset from a specified URL and loading it into R. The dataset comprises user demographic data and their purchasing behavior.

# Define the direct download URL for the dataset
download_url <- "https://drive.google.com/uc?export=download&id=183CuUb08gcK5s3Sf1OToDTu-ZYn-89pX"

# Download the dataset file to the local directory
download.file(download_url, destfile = "Social_Network_Ads.csv", mode = "wb")

# Read the dataset into R
dataset <- read.csv('Social_Network_Ads.csv')

Data Exploration

We begin by exploring the dataset to gain insights. This includes examining statistical summaries, data structure, correlation analysis, and visualizations of key variables.

Summary Statistics

summary(dataset)

Data Structure

str(dataset)

Correlation Analysis

cor(dataset[, c("Age", "EstimatedSalary", "Purchased")])

Data Visualization

We create various visualizations to better understand the data:

Age Distribution

# Visualization of the age distribution
ggplot(dataset, aes(x = Age)) +
  geom_histogram(binwidth = 1, fill = "blue", color = "black") +
  ggtitle("Age Distribution") +
  xlab("Age") +
  ylab("Frequency")

Estimated Salary Distribution

# Visualization of the estimated salary distribution
ggplot(dataset, aes(x = EstimatedSalary)) +
  geom_histogram(binwidth = 5000, fill = "green", color = "black") +
  ggtitle("Estimated Salary Distribution") +
  xlab("Estimated Salary") +
  ylab("Frequency")

Relationship Between Age and Estimated Salary

# Scatter plot to visualize the relationship between age and estimated salary
ggplot(dataset, aes(x = Age, y = EstimatedSalary, color = factor(Purchased))) +
  geom_point() +
  ggtitle("Relationship Between Age and Estimated Salary") +
  xlab("Age") +
  ylab("Estimated Salary") +
  scale_color_manual(values = c("red", "green"), labels = c("Not Purchased", "Purchased"))

Class Balance for the Target Variable 'Purchased'

# Visualization of class balance for the target variable 'Purchased'
ggplot(dataset, aes(x = factor(Purchased))) +
  geom_bar(fill = "orange", color = "black") +
  ggtitle("Class Balance for Purchases") +
  xlab("Purchased (0 = No, 1 = Yes)") +
  ylab("Frequency")

Gender Distribution

# Visualization of gender distribution
ggplot(dataset, aes(x = Gender)) +
  geom_bar(fill = "purple", color = "black") +
  ggtitle("Gender Distribution") +
  xlab("Gender") +
  ylab("Frequency")

Data Preprocessing

To prepare the data for modeling, we select relevant columns, encode the 'Purchased' variable as a factor, split the dataset into training and test sets, and perform feature scaling.

# Select relevant columns: Age, EstimatedSalary, and Purchased
dataset <- dataset[3:5]

# Encode the 'Purchased' variable as a factor
dataset$Purchased <- factor(dataset$Purchased, levels = c(0, 1))

# Split dataset into Training and Test sets with a 75% split ratio
set.seed(123)  # Ensure reproducibility
split <- sample.split(dataset$Purchased, SplitRatio = 0.75)
training_set <- subset(dataset, split == TRUE)
test_set <- subset(dataset, split == FALSE)

# Apply feature scaling to the Age and EstimatedSalary columns
training_set[-3] <- scale(training_set[-3])
test_set[-3] <- scale(test_set[-3])

Model Evaluation

We define an evaluation function to assess model performance using accuracy and Cohen's Kappa. Then, we train two models: K-Nearest Neighbors (K-NN) and Random Forest.

# Define a function to evaluate and return model performance metrics

evaluate_model <- function(predictions, actual) {
  cm <- confusionMatrix(as.factor(predictions), as.factor(actual))
  return(list(accuracy = cm$overall['Accuracy'], kappa = cm$overall['Kappa']))
}

# Fit a K-Nearest Neighbors (KNN) model to the training data.

knn_pred <- knn(train = training_set[, -3], test = test_set[, -3], cl = training_set[, 3], k = 5)

# Fit a Random Forest classifier to the training data.

set.seed(123)  # Set seed again for consistency in random forest results.
rf_classifier <- randomForest(x = training_set[-3], y = training_set$Purchased, ntree = 500)

# Use the fitted Random Forest classifier to make predictions on the test set.

rf_pred <- predict(rf_classifier, newdata = test_set[-3])

# Evaluate the performance of the KNN model.

knn_performance <- evaluate_model(knn_pred, test_set$Purchased)

# Evaluate the performance of the Random Forest model in a similar manner.

rf_performance <- evaluate_model(rf_pred, test_set$Purchased)

# Print out accuracy and kappa statistics for both models
cat("K-NN Accuracy:", knn_performance$accuracy, "Kappa:", knn_performance$kappa, "\n")
cat("Random Forest Accuracy:", rf_performance$accuracy, "Kappa:", rf_performance$kappa, "\n")

# Determine and print which model is more efficient based on accuracy
efficient_model <- ifelse(knn_performance$accuracy > rf_performance$accuracy, "K-NN", 
                          ifelse(knn_performance$accuracy < rf_performance$accuracy, "Random Forest", "Both"))
cat("The more efficient model based on accuracy is:", efficient_model, "\n")

# Compare models based on Kappa statistic and print the result
best_kappa_model <- ifelse(knn_performance$kappa > rf_performance$kappa, "K-NN", 
                           ifelse(knn_performance$kappa < rf_performance$kappa, "Random Forest", "Both"))
cat("The model with the better Kappa score is:", best_kappa_model, "\n")

Results

We visualize the classification results for both models using ggplot2 and compare their performance metrics in a bar chart.

K-NN Classification Results

# Visualize K-NN classification results
print(plot_model_results(test_set, knn_pred, "K-NN"))

Random Forest Classification Results

# Visualize Random Forest classification results
print(plot

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