<jupyter_start><jupyter_text>**Data Analytics Salary Prediction**<jupyter_code>import numpy as np 
import pandas as pd 
import matplotlib.pyplot as plt 
import seaborn as sns 
import plotly.express as px
df = pd.read_csv('Salary Dataset.csv')
df.head()
df.describe()
df.info()
df.isna().sum()
df['Job Title'].value_counts()
df['Location'].unique()<jupyter_output><empty_output><jupyter_text>Processing in Salary Column<jupyter_code>df['Salary'].apply(lambda x : x.split('/')[-1]).unique()
df['Salary'].apply(lambda x : x[0]).unique()
def convert(salary):
    Currency = salary[0]
    fre = salary.split('/')[-1]
    fsalary = float(salary.split('/')[0][1:].replace(',', '').replace('FN', ''))
    
    if Currency == '₹' :
        fsalary *= 0.013  
    elif Currency == '£' :
        fsalary *= 1.36  
    elif Currency == 'A' :
        fsalary *= 0.011  
        
    if fre == 'mo':
        fsalary *= 12   
    elif fre == 'hr':
        fsalary *= 1920 
    
    return fsalary
df['Salary ($/yr)'] = df['Salary'].apply(convert)
df.drop('Salary', axis=1, inplace=True)
df.head(10)
df.describe().round(3)
df['Job Title'].unique()
df.columns
avg_salary = df.groupby('Location').mean().reset_index()
avg_salary<jupyter_output><empty_output><jupyter_text>**EDA AND DATA VISUALIZATION**  Plotting Average Salary per year group by Location<jupyter_code>px.bar(data_frame=avg_salary,x='Location',y='Salary ($/yr)',color='Location',title='Average Salary per year group by Location')<jupyter_output><empty_output><jupyter_text>Plotting the average Salaries for each job<jupyter_code>plt.figure(figsize=(20, 10))
df.groupby('Job Title')['Salary ($/yr)'].mean().sort_values(ascending=False).plot(kind='bar')<jupyter_output><empty_output><jupyter_text>Plotting the distribution of top 6 Job<jupyter_code>plt.figure(figsize=(10, 10))
top5jobs = df['Job Title'].value_counts().sort_values(ascending=False).head(6)
top5jobs.plot(kind='pie')<jupyter_output><empty_output><jupyter_text>Plotting the average Salaries for the top 10 companies<jupyter_code>plt.figure(figsize=(20, 10))
df.groupby('Company Name').mean()['Salary ($/yr)'].sort_values(ascending=False).head(10).plot(kind='bar')<jupyter_output><empty_output><jupyter_text>Plotting average salaries for each location<jupyter_code>plt.figure(figsize=(20, 10))
df.groupby('Location').mean()['Salary ($/yr)'].sort_values(ascending=False).plot(kind='bar')
plt.show()<jupyter_output><empty_output><jupyter_text>Plotting top 6 high paying companies<jupyter_code>x = df.groupby('Company Name').mean()['Salary ($/yr)'].sort_values(ascending=False).head(6)
fig = px.bar(x, title="Top 6 High Paying Companies")
fig.update_traces(textfont_size=12, textangle=0, textposition="outside", cliponaxis=False)
fig.show()<jupyter_output><empty_output><jupyter_text>Plotting top 6 less paying companies<jupyter_code>x = df.groupby('Company Name').mean()['Salary ($/yr)'].sort_values(ascending=True).head(5)
fig = px.bar(x, title="Top 5 Less Paying Companies")
fig.update_traces(textfont_size=12, textangle=0, textposition="outside", cliponaxis=False)
fig.show()<jupyter_output><empty_output>
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