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EDA and Model Building on Popular Datasets

1. Iris Dataset :
A classification problem with zero missing values help us to get familiar with syntax.

2. Loan Prediction :
A classification problem but this time with some missing values. So imputed the missing values with various strategies.

3. Sms Spam Classification :
Time to get familier with NLP stuff. Using NLP a classification problem is solved.

4. Bigmart Sales Data Set :
A missing values could be imputed from another column. A different strategy is used to impute missing values.
A regression problem.

5. Boston Housing Data Set :
A regression solved with higher order polynomial values