Ad-Click-Prediction-on-internet-accessing-users

• Predicting whether or not a user will click on an ad, based on his/her features. As this is a binary classification problem, a logistic regression model is well suited here.
• Dataset used is advertising dataset which is of the ".csv" format.

Dataset:

• 'Daily Time Spent on Site': consumer time on site in minutes
• 'Age': cutomer age in years
• 'Area Income': Avg. Income of geographical area of consumer
• 'Daily Internet Usage': Avg. minutes a day consumer is on the internet
• 'Ad Topic Line': Headline of the advertisement
• 'City': City of consumer
• 'Male': Whether or not consumer was male
• 'Country': Country of consumer
• 'Timestamp': Time at which consumer clicked on Ad or closed window
• 'Clicked on Ad': 0 or 1 indicated clicking on Ad

Exploratory Data Analysis:

• Used seaborn jointplot and pairplot for analysing data.

Data splitting for training and testing:

• Used "train_test_split" from scikit-learn library for splitting the dataset into training and testing data.
• Data split is in the fraction of 0.3 for testing and 0.7 for training.

Model Training:

• Model is trained over the Logistic Regression Model.

Evaluations:

• "classification_report" is generated which gives the values of precision, recall, f1-score and support

Final Result:

• Precision = 92%
• Recall = 92%
• F1-score = 92%