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| import streamlit as st | |
| import pandas as pd | |
| import seaborn as sns | |
| import matplotlib.pyplot as plt | |
| import plotly.express as px | |
| from PIL import Image | |
| from wordcloud import WordCloud | |
| from wordcloud import ImageColorGenerator | |
| from wordcloud import STOPWORDS | |
| st.set_page_config( | |
| page_title = 'Data Science Salary Estimator' | |
| ) | |
| def run(): | |
| # Membuat Title | |
| st.title('Data Science Salary Estimator') | |
| #Sub header | |
| st.subheader('Description for Data Science Salary Estimator') | |
| # Insert Gambar | |
| image = Image.open('gaji.jpg') | |
| st.image(image, caption ='SALARY') | |
| #description | |
| st.write('The goals of this salary estimator') | |
| st.write('as a data scientist i want to know if im getting the decent salary from the company, so i created this machine learning model to predict salary for jobs in data world.') | |
| st.write('I hope with this salary estimator can help fellas data to see if they also get a decent salary or not') | |
| st.markdown('---') | |
| # Menambahkan Deskripsi | |
| st.write('This page is created to show the visualization of the dataset') | |
| st.markdown('---') | |
| st.write('Description') | |
| st.write('Experience Level') | |
| st.write('EN, which is Entry-level. MI, which is Mid-level. SE, which is Senior-level. EX, which is Executive-level.') | |
| st.write('Employment Type') | |
| st.write('FT, which is Full Time. PT, which is Part Time. CT, which is Contract. FL, which is Freelance.') | |
| st.write('Remote Ratio') | |
| st.write('100, which is Full remote. 50, which is hybrid. 0, which is on site.') | |
| st.markdown('---') | |
| #show dataframe | |
| data = pd.read_csv('https://raw.githubusercontent.com/FerdiErs/SQL/main/DataScienceSalaries.csv') | |
| st.dataframe(data) | |
| #membuat histogram salary | |
| st.write('### Histogram Salary') | |
| fig = plt.figure(figsize=(10,5)) | |
| sns.histplot(data['salary_in_usd'], kde=True, bins=40) | |
| plt.title('Histogram of salary in usd') | |
| st.pyplot(fig) | |
| #membuat pie chart experience | |
| st.write('### Experince Distribution') | |
| exp = data.experience_level.value_counts() | |
| def make_autopct(values): | |
| def my_autopct(pct): | |
| total = sum(values) | |
| val = int(round(pct*total/100.0)) | |
| return '{p:.2f}% ({v:d})'.format(p=pct,v=val) | |
| return my_autopct | |
| fig = plt.figure(figsize=(5,5)) | |
| exp.plot.pie(autopct=make_autopct(exp)) | |
| plt.title('Experince Level Distribution') | |
| st.pyplot(fig) | |
| #barchart posisi dengan gaji terbesar | |
| st.write('### 5 Role with highest paycheck') | |
| work_rate = data.groupby(['job_title'])['salary_in_usd'].mean() | |
| work = work_rate.nlargest(5) | |
| fig = plt.figure(figsize=(15,5)) | |
| work.plot(kind = "bar") | |
| plt.title('5 Role with Highest Paycheck') | |
| st.pyplot(fig) | |
| # negara dengan gaji tertinggi | |
| st.write('### Country with highest paycheck') | |
| location_payrate = data.groupby(['company_location'])['salary_in_usd'].sum() | |
| lar = location_payrate.nlargest(5) | |
| fig = plt.figure(figsize=(15,5)) | |
| lar.plot(kind = "bar") | |
| plt.title('5 Countries Highest Paycheck') | |
| st.pyplot(fig) | |
| # popular job | |
| st.write('### TOP 10 JOBS') | |
| job = data.groupby(['job_title'])['job_title'].count() | |
| top_job = job.nlargest(10) | |
| fig = plt.figure(figsize=(12,6)) | |
| plt.xticks(rotation=0) | |
| plt.title("Top 10 Jobs") | |
| plt.ylabel('Job Titles') | |
| plt.xlabel('Counts') | |
| sns.barplot(y=top_job.index, x= top_job.values) | |
| st.pyplot(fig) | |
| #wordcloud | |
| # see most job with word cloud | |
| text = " ".join(i for i in data.job_title) | |
| stopwords = set(STOPWORDS) | |
| wordcloud = WordCloud(width=1600, height=800).generate(text) | |
| fig = plt.figure( figsize=(15,10), facecolor='k') | |
| plt.imshow(wordcloud, interpolation='bilinear') | |
| plt.axis("off") | |
| st.pyplot(fig) | |
| if __name__== '__main__': | |
| run() |