question archive Problem 1: You are hired by one of the leading news channel CNBE who wants to analyse recent elections

Problem 1: You are hired by one of the leading news channel CNBE who wants to analyse recent elections

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Problem 1:

You are hired by one of the leading news channel CNBE who wants to analyse recent elections. This survey was conducted on 1525 voters with 9 variables. You have to build a model, to predict which party a voter will vote for on the basis of the given information, to create an exit poll that will help in predicting overall win and seats covered by a particular party.

Dataset for Problem 1: Election_Data.xlsx

**Data Dictionary**

 

1. vote: Party choice: Conservative or Labour

 

2. age: in years

 

3. economic.cond.national: Assessment of current national economic conditions, 1 to 5.

 

4. economic.cond.household: Assessment of current household economic conditions, 1 to 5.

 

5. Blair: Assessment of the Labour leader, 1 to 5.

 

6. Hague: Assessment of the Conservative leader, 1 to 5.

 

7. Europe: an 11-point scale that measures respondents' attitudes toward European integration. High scores represent ‘Eurosceptic’ sentiment.

 

8. political.knowledge: Knowledge of parties' positions on European integration, 0 to 3.

 

9. gender: female or male.

 

1.1) Read the dataset. Do the descriptive statistics and do null value condition check. Write an inference on it.

1.2) Perform Univariate and Bivariate Analysis. Do exploratory data analysis. Check for Outliers.

1.3) Encode the data (having string values) for Modelling. Is Scaling necessary here or not? Data Split: Split the data into train and test (70:30).

1.4) Apply Logistic Regression and LDA (Linear Discriminant Analysis).

1.5) Apply KNN Model, Naïve Bayes Model and Support Vector Machine (SVM) model.

1.6) Model Tuning, Bagging and Boosting.

1.7) Performance Metrics: Check the performance of Predictions on Train and Test sets using Accuracy, Confusion Matrix, Plot ROC curve and get ROC_AUC score for each model. Final Model: Compare the models and write inference which model is best/optimized.

1.8) Based on these predictions, what are the insights?

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