DATA SCIENCE
INTERNSHIP
DATA PREPARATION
HANDLE MISSING VALUES: check for missing values in the dataset and apply appropriate techniques
to handle them (e.g., Interpolation, deletion).
DATA SCALING: normalize or standardize the variables to ensure they are on a similar scale, if required.
OUTLIER DETECTION: identify and handle outliers using suitable techniques (e.G., Z-score,
interquartile range) to ensure they don't skew the analysis.
ANALYSIS STRATEGY
UNSUPERVISED ANOMALY DETECTION: utilize an unsupervised anomaly detection algorithm to
identify abnormal periods in the data.
ISOLATION FOREST ALGORITHM: choose the isolation forest algorithm due to its effectiveness in
detecting anomalies in high-dimensional datasets.
MODEL TRAINING AND PREDICTIONS: train the isolation forest model on the selected variables and
predict anomalies for each time point in the dataset.
THRESHOLD DETERMINATION: determine an appropriate threshold for anomaly detection based on
the contamination parameter or other statistical measures.
VISUALIZATION: visualize the identified anomalies to gain insights into the abnormal periods.
INSIGHTS
HIGHLIGHTED ANOMALOUS PERIODS: display the time periods where anomalies were detected
in each of the variables.
ABNORMAL OPERATION PATTERNS: discuss any common patterns or correlations observed
during abnormal periods across the variables.
ROOT CAUSES AND IMPLICATIONS: explore potential reasons behind the abnormal operations
and their potential impact on the cyclone preheater system.
RECOMMENDATIONS: provide suggestions for actions or interventions to address the identified
abnormal periods and improve the overall performance of the cyclone preheater.
THANK YOU