https://github.com/blurred-machine/ad-click-prediction-on-internet-accessing-users
Predicting whether or not a internet accessing user will click on an ad, based on his/her features.
https://github.com/blurred-machine/ad-click-prediction-on-internet-accessing-users
Last synced: 6 days ago
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Predicting whether or not a internet accessing user will click on an ad, based on his/her features.
- Host: GitHub
- URL: https://github.com/blurred-machine/ad-click-prediction-on-internet-accessing-users
- Owner: blurred-machine
- Created: 2019-12-03T14:58:11.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-12-03T15:13:15.000Z (over 6 years ago)
- Last Synced: 2025-02-26T12:15:29.483Z (over 1 year ago)
- Language: Jupyter Notebook
- Size: 406 KB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# 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%