This Assignment involves:

• Convertng the dataset 'data.csv' into balanced class dataset.
• Creating five samples using five different sampling techniques.
• Applying five different ML models on them.
• Determining which sampling technique gives higher accuracy on which model.

• Simple Random Sampling- Pick the sample at random

• Systematic Sampling- Samples are chosen at random intervals.
• Stratified Sampling- The population is divided into subgroups or strata based on a certain characteristic. Individual elements from a sub-population can be randomly selected.

• Cluster Sampling- The entire population is divided into smaller groups and then a random sample of these clusters is selected. The sample size is then selected on the basis of sample size.

• KNN
• Logistic Regression
• Naive Bayes
• Support Vector Machine
• Decision Tree