Rapid Fire on Machine Learning

Machine learning models are about making accurate predictions about the situations, like Foot Fall in restaurants, Stock-Price, etc. where-as, Statistical models are designed for inference about the relationships between variables, as What drives the sales in a restaurant, is it food or Ambience. Statistical modeling and machine learning are both approaches used in data analysis, … Read more

Algorithms necessitate features with some specific characteristics to work appropriately. The data is initially in a raw form. You need to extract features from this data before supplying it to the algorithm. This process is called feature engineering. When you have relevant features, the complexity of the algorithms reduces. Then, even if a non-ideal algorithm … Read more

Exploratory Data Analysis (EDA) helps analysts to understand the data better and forms the foundation of better models. Visualization Univariate visualization Bivariate visualization Multivariate visualization Missing Value Treatment – Replace missing values with Either Mean/Median Outlier Detection – Use Boxplot to identify the distribution of Outliers, then Apply IQR to set the boundary for IQR … Read more

Sampling Techniques can help with an imbalanced dataset. There are two ways to perform sampling, Under Sample or Over Sampling. In Under Sampling, we reduce the size of the majority class to match minority class thus help by improving performance w.r.t storage and run-time execution, but it potentially discards useful information. For Over Sampling, we … Read more

Temporal Difference Learning Method is a mix of Monte Carlo method and Dynamic programming method. Some of the advantages of this method include: It can learn in every step online or offline. It can learn from a sequence which is not complete as well. It can work in continuous environments. It has lower variance compared … Read more