In simpler words we tell whether a user on Social Networking site after clicking the ad’s displayed on the website,end’s up buying the product or not. This could be really helpful for the company selling the product. Lets say that its a car company which has paid the social networking site(For simplicity we’ll assume its Facebook from now on)to display ads of its newly launched car.Now since the company relies heavily on the success of its newly launched car it would leave no stone unturned while trying to advertise the car. Well then whats better than advertising it on the most popular platform right now.But what if we only advertise it to the correct crowd.This could help in boosting sales as we will be showing the ad of the car only to selected crowd. So this is where you come in… The Car company has hired you as a Data Scientist to find out the correct crowd, to which you need to advertise the car and find out the people who are most likely to buy the car based on certain features which describe the type of users who have bought the car previously by clicking on the ad.
This was build using following frameworks, libraries and softwares.
| Classifier | Preprocessing | Confusion Matrix | Code |
|---|---|---|---|
| Logistic Regression | Splitting dataset, Feature Scaling | Code | |
| SVM | Splitting dataset, Feature Scaling | Code | |
| Kernel SVM | Splitting dataset, Feature Scaling | Code | |
| Naive Bayes | Splitting dataset, Feature Scaling | Code | |
| K Nearest Neighbours | Splitting dataset, Feature Scaling | Code | |
| Decision Tree | Splitting dataset, Feature Scaling | Code | |
| Random Forest | Splitting dataset, Feature Scaling | Code |
- Big Data Is Complicated
- Predictive Analysis Is Intelligent
- Anticipation Is Better Than Reaction
- It Optimizes for Micro-Moments
- It’s Cost-Effective
For more examples, please refer to the Article
See the open issues for a list of proposed features (and known issues).
Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
- MIT license
- Copyright 2020 © Aditya Mangla.
Aditya Mangla - @aadimangla - aadimangla@gmail.com - adityamangla.com
Project Link: https://github.com/aadimangla/Predicting-product-sales-through-ads