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.
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.
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.
- Data Preprocessing: Cleaning and preparing the dataset for analysis.
- Feature Engineering: Identifying relevant features such as age, gender, estimated salary, and other demographics.
- Model Building: Applying classification algorithms to predict ad engagement.
- Evaluation: Evaluating the model’s accuracy using metrics like confusion matrix, precision, recall, and F1-score.
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)
-
Clone this repository:
git clone /~https://github.com/yourusername/Predictive-Analysis-of-Social-Network-Advertisements.git
-
Navigate to the project directory:
cd Predictive-Analysis-of-Social-Network-Advertisements
-
Install the required dependencies:
pip install -- upgrade -r requirements.txt
- Data Cleaning & Preprocessing: Removing any missing values and standardizing the input features.
- Exploratory Data Analysis (EDA): Visualizing relationships between the features and the target variable.
- Feature Selection: Using correlation and statistical methods to select the most relevant features.
- Model Selection: Trying different machine learning models like Logistic Regression, Decision Trees, and Random Forest to determine the best performer.
- Evaluation: Measuring the model's performance with metrics like accuracy, precision, recall, and ROC-AUC score.
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
- Python
- Pandas
- NumPy
- Scikit-Learn
- Matplotlib & Seaborn (for visualizations)
- Jupyter Notebook
- Additional Features: Integrating additional features such as user behavior, device type, and time of day could improve model accuracy.
- Hyperparameter Tuning: Using GridSearchCV or RandomizedSearchCV for optimizing the model’s parameters.
- Model Deployment: Deploying the model using a web interface or cloud service to provide real-time predictions.
Contributions are welcome! If you'd like to contribute, please fork the repository and make your changes via a pull request.
This project is licensed under the MIT License. See the LICENSE file for more details.