<jupyter_start><jupyter_text>Load Libraries<jupyter_code>import numpy as np 
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import model_selection, linear_model, tree
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
from sklearn.neighbors import KNeighborsRegressor
import warnings; warnings.simplefilter('ignore')
%matplotlib inline
sns.set()
seed = 1234
inputDataset="5c8c31db3603991515a6e2da" # Recommendations Input Dataset<jupyter_output><empty_output><jupyter_text>Load Data<jupyter_code>from data_access_sdk_python.reader import DataSetReader
from datetime import date
reader = DataSetReader()
df = reader.load(data_set_id=inputDataset, ims_org="27FE52955C494A9C0A495D66@AdobeOrg")<jupyter_output><empty_output><jupyter_text>Summarize the Data  Dataset Dimensions<jupyter_code>print("We should see 100 instances and 4 columns", df.shape)<jupyter_output><empty_output><jupyter_text>Peeking at the Data<jupyter_code>df.head(20)<jupyter_output><empty_output><jupyter_text>Statistical Summary<jupyter_code>user_id = '_platformlab.userId'
item_id = '_platformlab.itemId'
interaction_type = '_platformlab.interactionType'
timestamp = '_platformlab.timestamp'
df.describe()<jupyter_output><empty_output><jupyter_text>Grouping Items by User ID<jupyter_code>df[[user_id, item_id]].groupby([item_id]).agg(['count'])<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.09213323883770376, 'fraction_numerical': 0.03047484053862509, 'mean_word_length': 11.607142857142858, 'pattern_counts': {'":': 0, '<': 25, '<?xml version=': 0, '>': 25, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '6', 'dir': 'jupyter-structured-clean-dedup', 'id': '241740', 'license': 'Apache-2.0', 'n_tokens_mistral': 528, 'n_tokens_neox': 492, 'n_words': 86, 'path': 'Summit/2019/resources/Notebooks-Thurs/recommendations-data-exploration.ipynb', 'repo_name': 'pawan-mittal/experience-platform-dsw-reference', 'size': '1411'}