missing value visualization added

Author: divaymohan

FakeJobsPradictor/.ipynb_checkpoints/NoteBook1-checkpoint.ipynb

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+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

FakeJobsPradictor/MissingValueCheck/NoteBook1.md

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+# Data Visualization
+[<-  Back to Home](../../README.md)
+
+```python
+import pandas as pd
+import numpy as np
+from plotly import express as px
+from matplotlib import pyplot as plt
+import missingno as msn
+import seaborn as sns
+%matplotlib inline
+```
+
+## Reading File
+
+
+```python
+df = pd.read_csv("fake_job_postings.csv")
+df.head(2)
+```
+
+
+
+
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>job_id</th>
+      <th>title</th>
+      <th>location</th>
+      <th>department</th>
+      <th>salary_range</th>
+      <th>company_profile</th>
+      <th>description</th>
+      <th>requirements</th>
+      <th>benefits</th>
+      <th>telecommuting</th>
+      <th>has_company_logo</th>
+      <th>has_questions</th>
+      <th>employment_type</th>
+      <th>required_experience</th>
+      <th>required_education</th>
+      <th>industry</th>
+      <th>function</th>
+      <th>fraudulent</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>1</td>
+      <td>Marketing Intern</td>
+      <td>US, NY, New York</td>
+      <td>Marketing</td>
+      <td>NaN</td>
+      <td>We're Food52, and we've created a groundbreaki...</td>
+      <td>Food52, a fast-growing, James Beard Award-winn...</td>
+      <td>Experience with content management systems a m...</td>
+      <td>NaN</td>
+      <td>0</td>
+      <td>1</td>
+      <td>0</td>
+      <td>Other</td>
+      <td>Internship</td>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>Marketing</td>
+      <td>0</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>2</td>
+      <td>Customer Service - Cloud Video Production</td>
+      <td>NZ, , Auckland</td>
+      <td>Success</td>
+      <td>NaN</td>
+      <td>90 Seconds, the worlds Cloud Video Production ...</td>
+      <td>Organised - Focused - Vibrant - Awesome!Do you...</td>
+      <td>What we expect from you:Your key responsibilit...</td>
+      <td>What you will get from usThrough being part of...</td>
+      <td>0</td>
+      <td>1</td>
+      <td>0</td>
+      <td>Full-time</td>
+      <td>Not Applicable</td>
+      <td>NaN</td>
+      <td>Marketing and Advertising</td>
+      <td>Customer Service</td>
+      <td>0</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+
+
+
+
+```python
+print(df.columns)
+```
+
+    Index(['job_id', 'title', 'location', 'department', 'salary_range',
+           'company_profile', 'description', 'requirements', 'benefits',
+           'telecommuting', 'has_company_logo', 'has_questions', 'employment_type',
+           'required_experience', 'required_education', 'industry', 'function',
+           'fraudulent'],
+          dtype='object')
+
+
+**Question 1 ::** How many Datapoints are present in the data?
+
+**Question 2 ::** How many Features are present in the data?
+
+
+```python
+print("sol1:- Total Number Of DataPoints:- {}.".format(df.shape[0]))
+print("sol2:- Total Number of features:- {}.".format(df.shape[1]))
+```
+
+    sol1:- Total Number Of DataPoints:- 17880.
+    sol2:- Total Number of features:- 18.
+
+
+**Question 3 ::** check for null values ?
+
+
+```python
+df.info()
+```
+
+    <class 'pandas.core.frame.DataFrame'>
+    RangeIndex: 17880 entries, 0 to 17879
+    Data columns (total 18 columns):
+    job_id                 17880 non-null int64
+    title                  17880 non-null object
+    location               17534 non-null object
+    department             6333 non-null object
+    salary_range           2868 non-null object
+    company_profile        14572 non-null object
+    description            17879 non-null object
+    requirements           15185 non-null object
+    benefits               10670 non-null object
+    telecommuting          17880 non-null int64
+    has_company_logo       17880 non-null int64
+    has_questions          17880 non-null int64
+    employment_type        14409 non-null object
+    required_experience    10830 non-null object
+    required_education     9775 non-null object
+    industry               12977 non-null object
+    function               11425 non-null object
+    fraudulent             17880 non-null int64
+    dtypes: int64(5), object(13)
+    memory usage: 2.5+ MB
+
+
+
+```python
+msn.matrix(df)
+```
+
+
+
+
+    <matplotlib.axes._subplots.AxesSubplot at 0x7f78397e4208>
+
+
+
+
+![png](output_8_1.png)
+
+
+
+```python
+msn.heatmap(df)
+```
+
+
+
+
+    <matplotlib.axes._subplots.AxesSubplot at 0x7f7833f10780>
+
+
+
+
+![png](output_9_1.png)
+
+
+
+```python
+msn.bar(df)
+```
+
+
+
+
+    <matplotlib.axes._subplots.AxesSubplot at 0x7f783a141940>
+
+
+
+
+![png](output_10_1.png)
+
+
+
+```python
+for item in df.columns:
+    print("{} uniques: {}".format(item,df[item].unique().size))
+```
+
+    job_id uniques: 17880
+    title uniques: 11231
+    location uniques: 3106
+    department uniques: 1338
+    salary_range uniques: 875
+    company_profile uniques: 1710
+    description uniques: 14802
+    requirements uniques: 11969
+    benefits uniques: 6206
+    telecommuting uniques: 2
+    has_company_logo uniques: 2
+    has_questions uniques: 2
+    employment_type uniques: 6
+    required_experience uniques: 8
+    required_education uniques: 14
+    industry uniques: 132
+    function uniques: 38
+    fraudulent uniques: 2
+
+
+## Features:- 
+* job_id: - Every job have a different id
+* title:-  Job have a title
+* location:- Location of job
+* department:- Job department(ex:- marketing etc)
+* salary_range:- range of salary
+* company_profile:- what actually campany do like it is food company or tech company.
+* description :- Full descripton of job
+* requirements :- What are the Requirements
+* benefits:- What are the extra benifit
+* Telecommuting:- binary variable 
+* has_company_logo:- binary variable
+* has_questions:- binary variable 
+* employment_type:- full-time or part time 
+* required_experience:- internship of how much experience needed
+* required_education:- Minimum qualification  
+* industry:- Ex-marketing and advrtisement
+* function:- functionality of job
+* fraudulent:- it is fraud or not
+
+**Question 4 ::** How many datapoints Are Fraudent in the given data ?
+
+
+
+```python
+print("sol :: Number Of Fraudent job :: {}".format(df['fraudulent'].mean() * df['fraudulent'].size))
+```
+
+    sol :: Number Of Fraudent job :: 866.0
+
+
+
+```python
+msn.dendrogram(df)
+```
+
+
+
+
+    <matplotlib.axes._subplots.AxesSubplot at 0x7f783a2177b8>
+
+
+
+
+![png](output_15_1.png)
+