Q:

Difference between Standard scaler and MinMaxScaler

What is the difference between MinMaxScaler and standard scaler.
MMS= MinMaxScaler(feature_range = (0, 1)) ( Used in Program1)
sc = StandardScaler() ( In another program they used Standard scaler and not minMaxScaler)

A:

MinMaxScaler(feature_range = (0, 1)) will transform each value in the column proportionally within the range [0,1]. Use this as the first scaler choice to transform a feature, as it will preserve the shape of the dataset (no distortion).
StandardScaler() will transform each value in the column to range about the mean 0 and standard deviation 1, ie, each value will be normalised by subtracting the mean and dividing by standard deviation. Use StandardScaler if you know the data distribution is normal.
If there are outliers, use RobustScaler(). Alternatively you could remove the outliers and use either of the above 2 scalers (choice depends on whether data is normally distributed)
Additional Note: If scaler is used before train_test_split, data leakage will happen. Do use scaler after train_test_split

A:

From ScikitLearn site: 

StandardScaler removes the mean and scales the data to unit variance.
  However, the outliers have an influence when computing the empirical
  mean and standard deviation which shrink the range of the feature
  values as shown in the left figure below. Note in particular that
  because the outliers on each feature have different magnitudes, the
  spread of the transformed data on each feature is very different: most
  of the data lie in the [-2, 4] range for the transformed median income
  feature while the same data is squeezed in the smaller [-0.2, 0.2]
  range for the transformed number of households.
StandardScaler therefore cannot guarantee balanced feature scales in
  the presence of outliers.
MinMaxScaler rescales the data set such that all feature values are in
  the range [0, 1] as shown in the right panel below. However, this
  scaling compress all inliers in the narrow range [0, 0.005] for the
  transformed number of households.

