yaswanthteja / hdsc_machine-learning_classification_managing_the_quality_metric_of_global_ecological_footprint

HDSC_Machine-Learning-Classification --

Managing the Quality Metric of Global Ecological Footprint

The dataset used in this course was obtained from the National Footprint and Biocapacity Accounts. It provides Ecological Footprint per capita data for years 1961-2016 in global hectares (gha). The National Footprint and Biocapacity Accounts (NFAs) measure the ecological resource use and resource capacity of nations from 1961 to 2016. The calculations in the National Footprint and Biocapacity Accounts are primarily based on United Nations data sets.

In this course, we will use the data to classify and predict the quality metrics (qascore) of the ecological footprint data for the different countries. This data includes total and per capita national biocapacity, the ecological footprint of consumption, the ecological footprint of production and total area in hectares.

• Data Source

Project

• Project Instructions for Tag-Along Project Stability of the Grid System

• Electrical grids require a balance between electricity supply and demand in order to be stable. Conventional systems achieve this balance through demand-driven electricity production. For future grids with a high share of inflexible (i.e., renewable) energy sources, the concept of demand response is a promising solution. This implies changes in electricity consumption in relation to electricity price changes. In this work, we’ll build a binary classification model to predict if a grid is stable or unstable using the UCI Electrical Grid Stability Simulated dataset.

• Dataset: https://archive.ics.uci.edu/ml/datasets/Electrical+Grid+Stability+Simulated+Data+

• It has 12 primary predictive features and two dependent variables.

Predictive features:

• 'tau1' to 'tau4': the reaction time of each network participant, a real value within the range 0.5 to 10 ('tau1' corresponds to the supplier node, 'tau2' to 'tau4' to the consumer nodes); 'p1' to 'p4': nominal power produced (positive) or consumed (negative) by each network participant, a real value within the range -2.0 to -0.5 for consumers ('p2' to 'p4'). As the total power consumed equals the total power generated, p1 (supplier node) = - (p2 + p3 + p4); 'g1' to 'g4': price elasticity coefficient for each network participant, a real value within the range 0.05 to 1.00 ('g1' corresponds to the supplier node, 'g2' to 'g4' to the consumer nodes; 'g' stands for 'gamma'); Dependent variables:

• 'stab': the maximum real part of the characteristic differential equation root (if positive, the system is linearly unstable; if negative, linearly stable); 'stabf': a categorical (binary) label ('stable' or 'unstable'). Because of the direct relationship between 'stab' and 'stabf' ('stabf' = 'stable' if 'stab' <= 0, 'unstable' otherwise), 'stab' should be dropped and 'stabf' will remain as the sole dependent variable (binary classification).

• Split the data into an 80-20 train-test split with a random state of “1”. Use the standard scaler to transform the train set (x_train, y_train) and the test set (x_test). Use scikit learn to train a random forest and extra trees classifier. And use xgboost and lightgbm to train an extreme boosting model and a light gradient boosting model. Use random_state = 1 for training all models and evaluate on the test set. Answer the following questions: