This project focuses on predicting user purchases of products displayed through social media advertisements. Leveraging the power of random forest classification, this system aims to forecast which users are more likely to make a purchase based on their interaction with social media ads.

The primary goal of this project is to enhance the efficiency of social media advertising by identifying potential buyers. By utilizing machine learning algorithms, specifically random forest classification, the system predicts user behavior regarding product purchases. This prediction capability enables targeted marketing strategies, optimizing ad placement and increasing conversion rates.

• Random Forest Classification: Utilizes the random forest algorithm to predict user purchase behavior.
• Data Analysis and Preprocessing: Includes exploratory data analysis and preprocessing techniques to prepare the dataset for modeling.
• Model Training and Evaluation: Trains the predictive model and evaluates its performance to ensure accuracy and reliability.
• Prediction: Enables the system to make predictions on whether a user is likely to purchase a product based on social media ad interactions.

To use this system:

1. Data Preparation: Ensure the dataset is properly formatted and preprocess it if necessary.
2. Model Training: Train the random forest classifier using the prepared dataset.
3. Prediction: Use the trained model to predict user purchases based on social media ad interactions.

To install and set up the project, follow these steps:

1. Clone the repository:

   git clone https://github.com/your_username/Social-Sales-Forecast-Improving-Social-Network-ads.git

2. Install dependencies:

   pip install -r requirements.txt

3. Run the project:

   python main.py

This project is licensed under the MIT License.