Understanding the problem statement

Importing Dataset directly from AWS

Understanding the attributes and their datatypes

Importing important libraries and understanding its significance

Using the summary function in R and interpreting its result

Time stamping the column with time attributes

Visualizing and understanding density plot

Plotting a time series plot

Understanding seasonality and trends

Plotting box plot and whiskers plot for visualizing outliers

Visualizing ggplot and Barplot

Filling out null values and feature engineering

Visualizing variables using panel graphs

Selecting the best feature using RFE(Recursive Feature Elimination)

Converting categorical into numerical vectors

Applying Boosting model Gradient Boosting Model for training

Applying linear model Linear Regression

Applying SVM using different Kernels

Selecting best evaluation metrics

Plotting graphs for visualizing the results

Selecting the best model for hyper-parameter tuning

Using Grid Search CV to extract the best features

Making final predictions and Saving them in form of CSV

Introduction to Problem Statement

05m

Dataset Overview

08m

Data PreProcessing

00m

Import Libraries

02m

Format Date

14m

Identify Missing Values

02m

Plotting univariate features

09m

Plotting bivariate features

06m

Identify Outliers

12m

Visualize energy usage

16m

Summary statistics using DPLYR

10m

Heat map for usage pattern

33m

Recap

02m

Model Data Preparation

03m

Correlations Table

07m

Feature Selection using Boruta

09m

Adding Dummy Variables to model

01m

Create Model using RFE control

08m

List chosen Features for prediction

02m

Training the Model

04m

Training the SVM and RF Model

03m

Conclusion

01m