4/21/22, 1:51 PM Classification Models project https://csus.instructure.com/courses/89590/assignments/ XXXXXXXXXX/1 You may have already started using help from Tutorial_6 in Labs on the dataset you...

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4/21/22, 1:51 PM Classification Models project https://csus.instructure.com/courses/89590/assignments/1414126 1/1 You may have already started using help from Tutorial_6 in Labs on the dataset you used in the previous project(s). However, in addition, please follow the instructions on the dataset provided for this project. You may use help from Tutorial_6. The more datasets you gain experience with, the deeper your insights are as each dataset is unique. *** This tutorial Tutorial_6 has examples for all Classification Models. Hence get started with Decision Trees and then keep doing other models one at a time. Submit after all classification models are taught. *** Please create a one or two page report summarizing the key approaches, decisions and findings. The Rubric is as follows: (It is important to have insights in differences between different datasets) 1. Running classification models we learned using help from Tutorial_6 on your dataset: 10% 2. Feature selection and Data splitting on the provided dataset: 25% 3. Running the classification models on the provided dataset: 25% 4. Performing accuracy analysis and comparing the models on the provided dataset: 25% 5. Any Visuals: 5% 6. Report: 10%
Answered 1 days AfterApr 21, 2022

Answer To: 4/21/22, 1:51 PM Classification Models project...

Suraj answered on Apr 23 2022
84 Votes
Classification Modelling for Churn Modelling Data
We are given the churn modelling data set to make
few machine learning classification model to predict on the dependent variable. The data set consist of 14 different variables. 3 variables are not important for the modelling as they are the simple index number or id of the customers. Hence, those variables are dropped from the data set.
Building any machine learning model, the first step is to make data clean that is capable for the analysis. The data set consist of two categorical variables. Those variables are the geography and gender of the customers. The variables are then converted to the numerical variables by coding each of the item in the variable. After this the data set is completely ready for the analysis purpose.
Now the next step is to make different classification model....
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