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