Simple_Linear_Regression Predicting Delivery Time Using Sorting Time Step 1 Importing Data Step 2 Performing EDA On Data a.) Renaming columns b.) Checking Datatype c.) Checking for Null Values d.) Checking for Duplicate Values Step 3 Plotting the data to check for outliers Step 4 Checking the Correlation between variables Step 5 Checking for Homoscedasticity or Hetroscedasticity Step 6 Feature Engineering a.) Trying different transformation of data to estimate normal distribution and to remove any skewness Step 7 Fitting a Linear Regression Model a.) Using Ordinary least squares (OLS) regression b.) Square Root transformation on data c.) Cube Root transformation on Data d.) Log transformation on Data Step 8 Residual Analysis a.) Test for Normality of Residuals (Q-Q Plot) b.) Residual Plot to check Homoscedasticity or Hetroscedasticity Step 9 Model Validation a.) Comparing different models with respect to their Root Mean Squared Errors Step 10 Predicting values from Model with Log Transformation on the Data Building a prediction model for Salary hike Building a simple linear regression model by performing EDA and do necessary transformations and select the best model using R or Python. Step 1 Importing Data Step 2 Performing EDA On Data a.) Checking Datatype b.) Checking for Null Values c.) Checking for Duplicate Values Step 3 Plotting the data to check for outliers Step 4 Checking the Correlation between variables Step 5 Checking for Homoscedasticity or Hetroscedasticity Step 6 Feature Engineering a.) Trying different transformation of data to estimate normal distribution and to remove any skewness Step 7 Fitting a Linear Regression Model a.) Using Ordinary least squares (OLS) regression b.) Square Root transformation on data c.) Cube Root transformation on Data d.) Log transformation on Data Step 8 Residual Analysis a.) Test for Normality of Residuals (Q-Q Plot) b.) Residual Plot to check Homoscedasticity or Hetroscedasticity Step 9 Model Validation a.) Comparing different models with respect to their Root Mean Squared Errors Step 10 Predicting values from Model with Log Transformation on the Data