Treatment of missing values Outlier detection at least two models trained and evaluated using k fold cross-validation

INSTRUCTIONS TO CANDIDATES

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Instructions

In this assessment, you will be implementing, testing, and documenting Predictive Analytics models for Fuel Economy dataset. You will develop predictive models (or combination of models) to predict fuel consumption (UCity) with respect to the other independent variables. 

Task 1: Exploratory Data Analysis: Correlation Analysis, Univariate, Bivariate analysis

Task 2: Model training and evaluations: Treatment of missing values, Outlier detection, at least two models trained and evaluated using k-fold cross-validation.

 

In the project report, include appropriate visualizations/plots with explanations showing your understanding of the dataset. You may choose to use a subset of best predicting variables. Apply k-fold cross-validation to ensure the generalizability of your models. Compare at least two learning algorithms. 

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