DATA SCIEN 2020Machine Learning Project

In [1]: import pandas as pd import numpy as np from sklearn import preprocessing from _selection import train_test_split from _bayes import GaussianNB from cs import accuracy_score import seaborn as sns import t as plt from import zscore import warnings rwarnings( "ignore") from r_model import LinearRegression from er import KMeans from cs import mean_squared_error from ers_influence import variance_inflation_fac tor import math from r_model import LogisticRegression from sklearn import metrics from cs import plot_confusion_matrix from cs import roc_auc_score,roc_curve,classification_repo rt,confusion_matrix #plot_confusion_matrix 1.1 Read the dataset. Do the descriptive statistics and do the null value condition check. Write an inference on it. In [2]: df=_csv("Election_D",index_col=0) In [3]: (10) Out[3]: vote age nal hold Blair Hague Europe politica 1 Labour 43 3 3 4 1 2 Create PDF in your applications with the Pdfcrowd HTML to PDF API PDFCROWD In [4]: (10) vote age nal hold Blair Hague Europe politica 2 Labour 36 4 4 4 4 5 3 Labour 35 4 4 5 2 3 4 Labour 24 4 2 2 1 4 5 Labour 41 2 2 1 1 6 6 Labour 47 3 4 4 4 4 7 Labour 57 2 2 4 4 11 8 Labour 77 3 4 4 1 1 9 Labour 39 3 3 4 4 11 10 Labour 70 3 2 5 1 11 Out[4]: vote age nal hold Blair Hague Europe 1516 Conservative 82 2 2 2 1 11 1517 Labour 30 3 4 4 2 4 1518 Labour 76 4 3 2 2 11 1519 Labour 50 3 4 4 2 5 1520 Conservative 35 3 4 4 2 8 1521 Conservative 67 5 3 2 4 11 1522 Conservative 73 2 2 4 4 8 1523 Labour 37 3 3 5 4 2 1524 Conservative 61 3 3 1 4 11 1525 Conservative 74 2 3 2 4 11 Create PDF in your applications with the Pdfcrowd HTML to PDF API P