Project 2 - EDA on Vehicle Insurance Customer Data and Proctored Viva Voce

During this project I have used following techniques for EDA on vehiccle insurance data.

1. Load csv file into python, different method for edit data frame.

2. Handling a missing values in proper way. ex- For numerical data missing value is replces with mean and For Categorical data missinng value is replced with mode.

3. Learnt about outliers, statistical as well as visulization method to deal with outliier.

4. Dara frame method: removing white spaces, Case correction.

5. Concept of Dummy variable that are used to label categorical data so it can be further used in ML application.

6. Merge 2 data frame based on one unique column.

7. With the help of group by function we have splitted a data into groups with certain conditions.

8. Found out co-relation between 2 feature.