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https://github.com/deepramazumder/social-network-ads-prediction-analysis
A Machine Learning project to predict user interactions with social network ads using demographic data to optimize ad targeting
https://github.com/deepramazumder/social-network-ads-prediction-analysis
ad-targeting batch-gradient-descent demographics logistic-regression mini-batch-gradient-descent stochastic-gradient-descent
Last synced: 4 days ago
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A Machine Learning project to predict user interactions with social network ads using demographic data to optimize ad targeting
- Host: GitHub
- URL: https://github.com/deepramazumder/social-network-ads-prediction-analysis
- Owner: DeepraMazumder
- License: mit
- Created: 2024-09-07T15:27:52.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2024-09-07T17:42:20.000Z (2 months ago)
- Last Synced: 2024-09-08T17:23:39.050Z (2 months ago)
- Topics: ad-targeting, batch-gradient-descent, demographics, logistic-regression, mini-batch-gradient-descent, stochastic-gradient-descent
- Language: Jupyter Notebook
- Homepage:
- Size: 633 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 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:
```bash
git clone https://github.com/yourusername/Predictive-Analysis-of-Social-Network-Advertisements.git
```
2. Navigate to the project directory:
```bash
cd Predictive-Analysis-of-Social-Network-Advertisements
```
3. Install the required dependencies:
```bash
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](LICENSE) file for more details.