1. IMPORTING THE DATASETS:
   Imported the training and testing datasets using pandas .read_csv() function.
   

2. EXPLORATORY DATA ANALYSIS:
   a)Placed all the missing values in a seperate list.
   b)Placed all thenumeric, categorical and discrete values in different lists.
   c)Visualized numerical features with line plot from matplotlib.
   d)Visualized continousfeatures with histogram from matplotlib.
   

3. FEATURE ENGINEERING:
   a) Replace all the missing values with the mean of their respective field.
   b) Encoded Object datatypes using Label Encoder from sklearn

4. FEATURE SCALING:
   a) Scaled all the data with standard scaler from sklearn.
   

5. MODEL FITTING:
   a) Split the data into training and test sets.
   b)Trained the model using XGBoost Regressor.
   c) hyperparameter tuning with RandomsearchCV.
   d) Stored the predictions in 'MySubmission.csv'.