added instacart example notebook

README.md

@@ -8,7 +8,7 @@ RankFM is a python implementation of the general Factorization Machines model cl
 
 The core training/prediction/recommendation subroutines are converted to optimized machine code at runtime using the [Numba](http:https://numba.pydata.org/) LLVM JIT compiler. This makes it possible to scale model training and recommendation to millions of user/item interactions. Designed for ease-of-use, RankFM accepts both `pd.DataFrame` and `np.ndarray` inputs. You do not have to convert your data to `scipy.sparse` matrices or re-map user/item identifiers to array indexes prior to use - RankFM internally maps all user/item identifiers to zero-based integer indexes, but always converts its outputs back to the original user/item identifiers from your data, which can be arbitrary (non-zero-based, non-consecutive) integers or even strings.
 
-In addition to the familiar `fit()`, `predict()`, `recommend()` methods, RankFM includes the additional utilities `similiar_users()` and `similar_items()` to find the most similar users/items to a given user/item based on latent factor space embeddings. A number of popular recommendation/ranking evaluation metric functions have been included in the separate `evaluation` module to streamline model tuning and validation. See the **Quickstart** section below to get started, and then the `/examples` folder for more in-depth walkthroughs using several popular open-source data sets in jupyter notebook format.
+In addition to the familiar `fit()`, `predict()`, `recommend()` methods, RankFM includes additional utilities `similiar_users()` and `similar_items()` to find the most similar users/items to a given user/item based on latent factor space embeddings. A number of popular recommendation/ranking evaluation metric functions have been included in the separate `evaluation` module to streamline model tuning and validation. See the **Quickstart** section below to get started, and the `/examples` folder for more in-depth jupyter notebook walkthroughs with several popular open-source data sets.
 
 This package is currently under active development and should not yet be considered fully stable. The core functionality is in place and working, but has not yet been rigorously tested against a wide variety of real-world data sets, modeling objectives, edge cases, user errors, etc. If you do find a problem or have suggestions for improvement please let me know!