Social Network Ads Prediction Analysis

This project focuses on building a machine learning model to predict user interactions with advertisements on social networks. By analyzing user demographics and ad-related data, the model predicts whether a given user is likely to click on a social media advertisement, helping businesses optimize their ad campaigns.

Introduction

Online advertising on social media platforms has become a critical marketing tool for businesses. However, ensuring the right audience engagement can be challenging. This project aims to address this issue by using machine learning techniques to predict whether a user will engage with an advertisement based on user data such as age, gender, and estimated salary.

Project Overview

The goal is to develop a model that can predict whether a user will click on an advertisement based on their demographic profile. This can assist marketers in targeting the most relevant users, improving ad efficiency and increasing conversions.

Key Steps:

1. Data Preprocessing: Cleaning and preparing the dataset for analysis.
2. Feature Engineering: Identifying relevant features such as age, gender, estimated salary, and other demographics.
3. Model Building: Applying classification algorithms to predict ad engagement.
4. Evaluation: Evaluating the model’s accuracy using metrics like confusion matrix, precision, recall, and F1-score.

Dataset

The dataset used contains user demographic information, including:

• Age: User's age
• Gender: Male or Female
• Estimated Salary: User's estimated annual salary
• Clicked on Ad: Binary feature indicating whether the user clicked on the advertisement (0 = No, 1 = Yes)

Installation

1. Clone this repository:

   git clone https://github.com/yourusername/Predictive-Analysis-of-Social-Network-Advertisements.git

2. Navigate to the project directory:

   cd Predictive-Analysis-of-Social-Network-Advertisements

3. Install the required dependencies:

   pip install -- upgrade -r requirements.txt

Approach

1. Data Cleaning & Preprocessing: Removing any missing values and standardizing the input features.
2. Exploratory Data Analysis (EDA): Visualizing relationships between the features and the target variable.
3. Feature Selection: Using correlation and statistical methods to select the most relevant features.
4. Model Selection: Trying different machine learning models like Logistic Regression, Decision Trees, and Random Forest to determine the best performer.
5. Evaluation: Measuring the model's performance with metrics like accuracy, precision, recall, and ROC-AUC score.

Results

The best model achieved an accuracy of 90% on the test set, with the following performance metrics:

• Precision: 92%
• Recall: 95%
• F1-Score: 93%
• ROC-AUC Score: 85.55

Technologies Used

• Python
• Pandas
• NumPy
• Scikit-Learn
• Matplotlib & Seaborn (for visualizations)
• Jupyter Notebook

Future Enhancements

1. Additional Features: Integrating additional features such as user behavior, device type, and time of day could improve model accuracy.
2. Hyperparameter Tuning: Using GridSearchCV or RandomizedSearchCV for optimizing the model’s parameters.
3. Model Deployment: Deploying the model using a web interface or cloud service to provide real-time predictions.

Contributing

Contributions are welcome! If you'd like to contribute, please fork the repository and make your changes via a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for more details.