- Price is a prominent factor in influencing the buying decisions of the people. Price optimization has become crucial for institutes to drive student and profitability.
- It also plays a major role in churn rate reduction of student.
- Price optimization reduces the manual work and minimizes the possibility of any human errors.
- Competitor Price behavior analysis is done which can assist Institutes for making better pricing decisions.
- First understanding about business problem.
- Listing out the various factors affecting the price prediction.
- Selecting important features for meeting the business target.
- Web scrapping through various Ed-Tech websites and enquiry.
- Handled noise data and generated random data.
- Considering all the necessary details and storing in the database.
Tech Used: Vs Code for working on models, Jupyter Notebook, Postgresql, Python, html, css
Libraries Used: Pandas for Data Manipulation, matplotlib and seaborn for data visualizaiton, make_pipe for making pipeline, column_transformer for encoding data before training sklearn for data preprocessing and model building, Flask for web application, and heroku for deployment.
Not much Preprocessing was needed, droped irrelevant columns
In order to get the best accuracy following models were used:
1. Random Forest Regressor
2. Linear Regression
3. Support Vector Regressor
4. KNeighbours Regressor
Out of which KNeighbours Regressor performed well with test accuracy of 99% and train accuracy of 98% and pickled for model deployment.
Model was deployed on Heroku.
Deployment link:- https://price-prediction-for-courses.herokuapp.com/