ClickPredictML

Predicting Ad Clicks

Project Summary: Data Analysis and Modeling for Click Prediction

Background: Goal is to analyze data related to ad clicks and predict whether a user will click on an ad.

Project Steps:

1. Data Exploration and Preprocessing:

   • Explored the dataset containing features like 'Daily Time Spent on Site,' 'Age,' 'Area Income,' 'Daily Internet Usage,' and more.
   • Conducted basic data preprocessing tasks such as handling missing values and categorical variables.

2. Exploratory Data Analysis (EDA):

   • Used various data visualization techniques to understand the relationships between features and the target variable ('Clicked on Ad').
   • Created correlation heatmaps, pair plots, and other visualizations to identify patterns and insights.

3. Data Modeling:

   • Chose the Logistic Regression and Random Forest models for classification due to their suitability for the problem.
   • Split the dataset into training and testing sets.
   • Utilized preprocessing techniques including standardization and scaling.

4. Model Evaluation:

   • Trained both models on the training data and evaluated their performance on the test data.
   • Calculated accuracy, precision, recall, and F1-score for both models.
   • Generated ROC curves and calculated AUC to assess model discrimination.

5. Hyperparameter Tuning:

   • Performed hyperparameter tuning for both Logistic Regression and Random Forest models using GridSearchCV.
   • Identified optimal hyperparameters to improve model performance.

6. Model Comparison and Selection:

   • Evaluated the performance of both models based on accuracy, precision, recall, and AUC.
   • Considered model interpretability, complexity, and generalization.

Results:

• Both models demonstrated strong performance, with accuracy scores around 96-99% on the test set.
• Logistic Regression achieved a slightly higher AUC, indicating better overall discrimination.
• Random Forest achieved higher training accuracy but exhibited slightly lower precision and recall compared to Logistic Regression.

Recommendation:

• The choice between Logistic Regression and Random Forest depends on your priorities:
  • If interpretability and simpler deployment are important, consider Logistic Regression due to its competitive performance and high interpretability.
  • If achieving a slightly higher accuracy is a priority, and you are willing to trade off some interpretability, Random Forest is a strong option.

Takeaway: Through this project, you've demonstrated your proficiency in data analysis, machine learning, and model evaluation. You've gained insights into different aspects of model performance and have the flexibility to choose a model based on the specific needs of your future roles. Your ability to explore, preprocess, model, and evaluate data showcases your skills in making data-driven decisions and could help set you apart in your career transition.