The following ML checklist has been followed in this project

1. Understand the problem statement

2. Import the required libraries

3. Fetch and read the required data set

4. Explore the data

5. Split the dataset using Stratified Splits [imp when dealing with small dataset]

   Stratified Splits: Split the dataset based on specified column where we want our train & test sets to have the same apporximate distribution. This needs to be done before any substantial visualization, in this way biases inherent can be avoided

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.33)
X_train

6. Visualize the data

   Data visualization is one of the most important part of the modelling process. Statistics are underpar in providing details about a dataset

   • Using Histogram

     foo.hist(bins=50, figsize=(20, 15))
     

   • Using Correlation Matrices

     corr = var.corr()
     mask = np.triu(np.ones_like(corr, dtype=bool))
     
      f, ax = plt.subplots(figsize=(11,9))
      cmap = sns.diverging_palette(230,20, as_cmap=True)
      sns.heatmap(corr, mask=mask, cmap=cmap, vmax=3, center=0, 
            square=True, linewidth=.5, cbar_kws={"shrink": .5})