Ads-Click-Through-Rate

Welcome to my Ads Click-Through Rate Prediction project! 🚀📊

Overview

In this project, I have developed a Machine Learning model to predict whether users will click on ads. By analyzing various characteristics of users who click on ads, we can gain valuable insights and optimize ad campaigns for better engagement.

Methods Used

• Exploratory Data Analysis (EDA): I used popular Python libraries such as pandas, NumPy, seaborn, and matplotlib to explore and understand the data. EDA helped me uncover patterns, relationships, and potential challenges in the dataset.

• Data Preprocessing: To prepare the data for modeling, I applied the following techniques: - Ordinal Encoding: I used the OrdinalEncoder to transform ordinal categorical features into numerical values. This allowed me to incorporate these features into the models effectively. - Standard Scaling: I employed the StandardScaler method to scale the numerical features. Scaling the features ensures that they are on a similar scale, preventing any particular feature from dominating the others during model training.

• Modeling: I experimented with multiple regression algorithms to find the best fit for the Ads Click-Through Rate Prediction task. The models I utilized include:

  Linear Regression: I started with a baseline Linear Regression model to establish a benchmark for comparison. Decision Tree Regressor: I explored the Decision Tree algorithm, which can capture non-linear relationships between features and the target variable. Random Forest Regression: I leveraged the power of ensemble learning with Random Forest Regression, which combines multiple decision trees to deliver more accurate predictions.

• Evaluation: To measure the accuracy and performance of the models, I used the mean squared error (MSE) metric. Additionally, I employed cross-validation scores to ensure the robustness of the models.

Requirements

To run this project locally, make sure you have the following requirements installed:

• Python
• pandas
• NumPy
• seaborn
• matplotlib
• scikit-learn You can install the required Python libraries by running the following command:

Copy code pip install pandas numpy seaborn matplotlib scikit-learn

Usage

Clone this repository to your local machine. Navigate to the project directory. Install the required dependencies as mentioned in the Requirements section. Run the main script or notebook to train the models and make predictions. Feel free to modify and experiment with the code to adapt it to your specific requirements.

Contributions

Contributions to this project are welcome! If you have any suggestions, improvements, or bug fixes, feel free to submit a pull request.