Handling missing values using machine learning (Linear Regression):

Handling missing values using machine learning:

Filling missing values using Linear Regression:

Step 1: Test data will be missing values
Step 2: Drop the null values and consider it as train data
Checking null values in train data:
Step 3:  Create x_train and y_train from the dataset
y_train is the rows of age with non null values
x_train is the dataset except age column with non null values

Step 4 : Building the model 

Step 5 :Creating X_test from Test_data 

Step 6: Applying the model and predicting the missing values

Importing Libraries:

 
Loading dataset:

Printing first 10 rows of dataset:

 
Number of rows and columns in the dataset:

 
Overall information about the dataset:

 
Name of the columns:

 
Statistically describing the dataset:

 
Number of null values:

 
Visualizing null values using heat map:
 

Step 1: Separating the null values and consider as test data

 
Step 2: Dropping the null values and considering as train data set

 
Checking null values:

 
Step 3: Creating X_train and Y_train from the dataset

 
X_train is the dataset except the 'Age' column:

 
Step 4 : Building the model

Training the model:

Step 5: Creating X_test from Test_data

 
Step 6: Applying the model and predicting the missing values

 
Replacing the missing values by predicted values: