Machine Learning business case studies solved using python. These are examples of how you can solve similar use cases for your own project and deploy the models into production.
I have discussed below points in each of the case studies.
- How to explore the given data?
- How to perform data pre-processing (missing values, outliers, transformations, etc.)
- How to create new columns based on existing columns (Feature Engineering)?
- How to select the best columns for machine learning (Feature Selection)?
- How to find the best ML algorithm for the given data?
- How to tune the predictive models.
- How to deploy predictive models into production?
- What happens after the model deployment?
Regression Use Cases
- Zomato restaurant rating
How to predict the future rating of a restaurant based on an ML model. A Case study in python. - Predicting diamond prices
Creating an ML model to predict the apt price of a given diamond. - Evaluating old car price
Predicting the right price for an old car using python machine learning. - Bike rental demand prediction
Create an ML model to forecast the demand of rental bikes every hour of the day. - Computer price prediction
Estimating the price of a computer, based on its specs. - Concrete strength prediction
How strong will this concrete be? Predicting the strength of concrete based on its mixture details. - Boston housing price prediction
House price prediction case study on the famous Boston data.
Classification Use Cases
- Loan Classification
A predictive model to approve/reject a new loan application. - German Credit Risk
Classification of a loan as a potential risk or safe for the bank. - Salary Band Classification
Identify if you deserve a salary more than $50,000 or not. - Titanic survival
A case study to see what type of passengers survived the titanic crash. - Advertisement Click Prediction
A case study to predict if a user will click on advertisements or not