ABOUT DATASET The dataset consists of various item which are brought by user in various transactions . The goal of the competition is to predict which products will be in a user’s next order. The dataset is anonymized and contains a sample of over7501 rows with 20 plus unique item…

Recommender System is an information filtering tool that seeks to predict which product a user will like, and based on that, recommends a few products to the users. For example, Amazon can recommend new shopping items to buy, Netflix can recommend new movies to watch, and Google can recommend news…

Implementation of linear regression through pyspark library in Databricks. before starting of the implementation we must familiar to the databricks platform. This is the homepage of the databricks developed specially for spark to implement on it and its is cost free. Before starting of the session we have to make…

FEATURE SELECTION TECNIQUES 1.variance threshold →constant →quasi constant →duplicate features 2.fisher score 3.chi-square test 4.mutual information # In[1]: #importig the libararies import pandas as pd import numpy as np # In[2]: bc_data=pd.read_csv(‘breast_cancer.csv’) # In[8]: bc_data.head() # In[10]: bc_data.shape # In[5]: bc_data.columns # In[6]: bc_data.info() # In[7]: bc_data.corr() # In[11]:

Create two data files with names and datasets as shown below and then implement the following queries using mrjob package in python for map reduce programming and using pigalso for the same.Compare the two processing types. We have dataset of employee and expenses in form of txt Employee.txt 101,Abhay,20000,1 102,Shiv,10000,2 …

Importing the libraries. import numpy as np import pandas as pd import matplotlib.pyplot as plt Reading the dataset df = pd.read_csv(“heart.csv”) df.head() #printing the all the columns with first 5 rows Dataset with independent and dependent variables. age age in years Independent Variable sex (1 = male; 0 = female) …

NASA ASTEROID PREDICTION DATASET FROM KAGGLE TOPICS TO BE COVERED: 1.KNN 2.SVC 3.RANDOM FOREST CLASSIFIER 4.ADA BOOST 5.GRADIENT BOSSTING 6.XG BOOST 7.FEATURE SELECTION,CROSS VALIDATION ,HYPERPARAMETER TUNING,RUC AUC GRAPH FOR ALL THE MODELS 8.ARTIFICAL NEURAL NETWORK (DEEP LEARNING) In [ ]: #Importing library import pandas as pd import matplotlib.pyplot as plt import numpy as np import seaborn as sns