acm-vit / good-client-bad-client Goto Github PK

Task
Compare the predicted and observed label classes to see the actual accuracy.

Task
Provide code to split sample data provided into two sets, training data and testing data.

• update description section
• upload dataset link
• mention steps to run code (under Usage)

Task
Define the feature variables which are to be predicted using this model

Function to implement
feature_creation()

Completed and merged your last 5 tasks? Great! By now, you must have learnt a lot but the real 'Machine Learning' begins from here. From now on, you will be provided with the topics you need to implement.

Task
Import packages for the model and prepare data
(Check the template code file for the topics above)

• make sure there is a template code set up
• for issues, you could create empty functions (for ex: def split(df) could return dataset split into test and train, and you could leave function description empty for collaborators to solve)

Congratulations! By now, you have successfully created a model and evaluated it, but is it the end? Of course not!
Let's optimize our model :)

Task

• Tune model parameters
  Considering, we have a relatively small size of the data and features, set high number of parameters for tuning.

• Optimize model classifier
  Fit the model with the tuned parameters and see the improvement in the accuracy of the model.

• Evaluate optimized model on testing sample
  Predict using the new-found accuracy!

Got your first PR merged? Awesome!
Continuing the task we started in our last issue:

Task
Try importing the Credit Card data set using the pandas package

Task
Check to see if there are any missing values in both the datasets imported.
If yes, then fill those missing values.

Functions to implement
missing_values_table(df)
solution_missing_values(df)

EDA and Vintage Analysis
Perform EDA for the data set to find best factors to be considered for the model.
What is Vintage Analysis could be searched here.

Where to show
Make all the Analysis under the Observation Heading.

Let's start on this project!

To create your first PR:

• Fork this repo
• Create a file with your name (yourname.py) in the folder submissions (Create one if folder is not there)
• Import the dataset and define the read_app_data() to read application data
• Make a pull request referencing this issue!

Function to Implement
read_app_data()

Task

• Define the ml model that you will be using in this project.
• Run the model on training data (Fitting)
• Predict outcomes using fitted model