Anomaly Detection
→ Identification of unusual patterns, outliers, which help us in understanding the variation in data.
Example:-
Association Rule Mining
→ Also referred to as market basket analysis, this method is used for discovering interesting "association" patterns among the variables.
Example :- The beer-diaper syndrome
Clustering
→ Identifying groups/classes in data which are similar to each other.
The similarity inside the "cluster" is high and between the "clusters" is low.
Classification
→ Classification is the process of identifying to which category does an observation belong.
Example:-
Regression
→ With the help of regression, we can identify the extent of relationship among variables.
Understanding how the "dependent" variable varies with respect to the variation in "independent" variable.
Who uses R?
1. FACEBOOK :- For behavior analysis related status updates and profile pictures.
2. GOOGLE :- For advertising effectiveness and economic forecasting.
3. TWITTER :- For data visualization and semantic clustering.
Tasks to be performed
→ Identification of unusual patterns, outliers, which help us in understanding the variation in data.
Example:-
Association Rule Mining
→ Also referred to as market basket analysis, this method is used for discovering interesting "association" patterns among the variables.
Example :- The beer-diaper syndrome
Clustering
→ Identifying groups/classes in data which are similar to each other.
The similarity inside the "cluster" is high and between the "clusters" is low.
Classification
→ Classification is the process of identifying to which category does an observation belong.
Example:-
Regression
→ With the help of regression, we can identify the extent of relationship among variables.
Understanding how the "dependent" variable varies with respect to the variation in "independent" variable.
Who uses R?
1. FACEBOOK :- For behavior analysis related status updates and profile pictures.
2. GOOGLE :- For advertising effectiveness and economic forecasting.
3. TWITTER :- For data visualization and semantic clustering.
Tasks to be performed
- Data Importing :- Import the "Houses for sale" dataset.
- Data Pre-processing :- Understand the structure of data and find correlation between different data entities.
- Data Mining :- Use Linear Regression to predict the rates of houses.
- Pattern Evaluation :- Evaluate which model fits better for the dataset.
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