MLOps Tools and Feature Engineering in Petrol Consumption

Problem Statement

Input Data:

Exploratory Data Analysis (EDA)

Table 1: Basic Statistics (N*= Missing values)
Figure 1: Histograms
Figure 2: Box Plots
Figure 3: Anderson-Darling Test for Paved Highways
Figure 4: Anderson-Darling Test for Average Income
Figure 5: Heatmap
Figure 6: Relation b/w Petrol Tax & Paved Highways
Figure 7: Line Plot b/w Percentage Population of Driver and Petrol Consumption

Feature Engineering

Creation of new features

Table 2: Income Range as New Feature
Table 3: Petrol Tax Range as a New Feature

Feature Scaling

Normalization (Min-Max )

Model Development

Step 1: Split the data into train and test

Step 2: Build ML model by passing the training data

Step 3: Model testing

Step 4: Model performance and evaluation metrics

Evaluation Metrics

Root Mean Squared Error

R- Square

Mean Absolute Error

Mean Squared Error (MSE)

Description of the ML Models

Model 1

Model 2

Model 3

Model 4

Model 5

Predictions of All Models

Table 4: Model Predictions

Model Evaluation Metrics

Table 5: Evaluation Metrics



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