image_output_1.png
from sklearn.preprocessing import LabelEncoder

import matplotlib.pyplot as plt

import pandas as pd

import seaborn as sns

train = pd.read_csv('../input/factors-affecting-campus-placement/Placement_Data_Full_Class.csv')

train.shape

corrMatrix = train.corr()

number = LabelEncoder()
train['status'] = number.fit_transform(train['status'].astype('str'))

number = LabelEncoder()
train['gender'] = number.fit_transform(train['gender'].astype('str'))

number = LabelEncoder()
train['workex'] = number.fit_transform(train['workex'].astype('str'))

corr_numeric = sns.heatmap(train[["status","mba_p","etest_p","hsc_p","degree_p","ssc_p", "gender", "workex"]].corr(),
                           annot=True, fmt = ".2f", cmap = "summer")

corr_numeric = sns.heatmap(train[["salary","mba_p","etest_p","hsc_p","degree_p","ssc_p", "gender", "workex"]].corr(),
                           annot=True, fmt = ".2f", cmap = "summer")

sns.barplot(x='specialisation', y='status', data=train)