Inspecting,Cleansing,Transforming and Modelling Data set using python.

There we have given the details of old cars and Price of cars respectively.

There will be following steps in data analysis:

1. Importing the Dataset.
2. Removing the columns or variable which are not necessary for output or target.
3. Handling Missing Values.
4. Formatting the Dataset or Particular columns in desired order.
5. converting the Datatype as Requirment.
6. Normalize the Data.
7. Dealing with Categorical Variable.
8. Visualising the Relation between target vector and Independent Variable using Scatter Plot, Box Plot, GroupBy, Heat Maps etc.
9. Corelation between the output and input vector using Reg plot, Pearson corelation, Anova(Analysis of Variance).
10. Fitting the Model like: a. Simple Linear Regression. b. Multiple Linear Regression. c. Polynomial Regression using Pipeline.
11. Insample Evaluation by calculating Mean Squared Error and R-Squared.
12. Splitting the dataset in training set and test set for better result.
13. Cross Validation for selecting the degree of polynomial regression by analysing the R-Squared Error.
14. Grid Search.