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Amount Withdrawn Model Part 2

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Univariate visualization features = exploration_dict [ 'possible_continuos_features' ] dataset [ features ] . hist ( figsize = ( 20 , 20 )); dataset [ features ] . plot ( kind = 'density' , subplots = True , layout = ( 4 , 3 ), sharex = False , figsize = ( 20 , 20 )); # Sometimes you can analyze an ordinal variable just as numerical one fig , axes = plt . subplots ( nrows = 3 , ncols = 4 , figsize = ( 30 , 30 )) for idx , feat in enumerate ( features ): ax = axes [ int ( idx / 4 ), idx % 4 ] sns . boxplot ( x = feat , data = dataset , ax = ax ) ax . set_xlabel ( feat ) ax . set_ylabel ( '' ) fig . tight_layout (); from pandas.plotting import scatter_matrix % config InlineBackend.figure_format = 'png' style . use ( 'tableau-colorbli...
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Hybrid Recommendation Engine

Hybrid Recommender System Intro In this project we are building a Hybrid recommender system which is a combination of 1. Content Based Filtering (Tag Based)  2. Collaborative Filtering Recommender System. The goal of this exercise is to recommend an item for a particular user based on the above recommender systems. We are going particularly implement the following paper in the link Work Flow of the Recommender System Like we mentioned the proposed system is comprised of 2 parts:  1. Data set Preparation 2. Collaborative Filtering Stage a. Creating User Profile based on User Related Tags b. Perform Clustering Based on Similarity Behind the Users  3. Content Based Filtering Stage a. Users from each cluster has similar preferences, so we perform Content Based Recommender within each cluster. b. Creating Item Profiles c. Perform Cosine Similarity Clustering between User Profiles and Item Profiles d. Calculate Tag Recency ...
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Amount Withdrawn Model Part 1

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% load_ext autoreload % autoreload 2 import sys sys . path . insert( 0 , '../../Models/ALS' ) from stark import * ;hello() / home / ec2 - user / SageMaker / SUNDARESAN M S / Scripts / HandsOnML - CookBook Data Science Environment Set ! from sklearn.model_selection import train_test_split from sklearn.model_selection import cross_val_score,KFold from sklearn.pipeline import make_pipeline from sklearn.metrics import mean_absolute_error,mean_squared_error from sklearn.metrics import make_scorer from sklearn.linear_model import ElasticNet, Lasso from sklearn.linear_model import LinearRegression from sklearn.tree import DecisionTreeRegressor from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor from xgboost import XGBRegressor from lightgbm import LGBMRegressor from matplotlib.pyplot import style import matplotlib.pyplot as plt import seaborn as sns import pandas as pd style . use( '...
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