Failure Prediction Model

Maximise Energy Company's Profits through Data Science

Objective and Focus

Machine Learning techniques to be used to predict part failures before they can occur

Data Profile

Weather data | For each site
Site and Sensor data
Revenue data | For each site
Repair costs | For each part repaired in the past (Available only in training data)

Approach

Requirements:

Python 3.7.0.
Pandas
sklearn
matplotlib.pyplot
seaborn
RandomForestRegressor
StandardScaler

Model Results

Prediction to fail results

Model trained using: Sensor data,Weather data
Model Type: Logistic Regression, Random Forest
Accuracy: 97.8%
Macro F1 Score: 69.3%