Freelancer logo
Log In Sign Up
Post a Project
EXPLORE
Data Analysis
Data Science
Power BI
Python
Profile cover photo
You're now following
Error following user.
This user does not allow users to follow them.
You are already following this user.
Your membership plan only allows 0 follows. Upgrade here.
Successfully unfollowed
Error unfollowing user.
You have successfully recommended
Error recommending user.
Something went wrong. Please refresh the page and try again.
Email successfully verified.
User Avatar

Dimas P.

@PashaAkrilian

5.0
1
0.6
0.6
100%

Data Scientist

$50 USD / Hour
・
Flag of
Indonesia (3:22 AM)
・
Joined on December 11, 2024
$50 USD / Hour
・
I am a software developer and data science expert with over 5 years of experience specializing in Python-based backend development and AI-driven solutions. My expertise spans machine learning, predictive analytics, pattern recognition, and natural language processing (NLP). I have extensive experience using frameworks such as Django and Flask, along with frontend development using React.js and Vue.js. In addition, I am highly skilled in data analysis and database management with PostgreSQL, API integration, and cloud services like AWS and DigitalOcean. I have successfully implemented AI-powered lead generation systems for B2C services, optimizing lead scoring and market analysis. With a strong focus on security, scalability, and continuous optimization, I am well-equipped to deliver high-performance, data-driven solutions for this project.
Verifications
On time
100%
On budget
100%
Accept rate
100%
Freelancer Python Developers Indonesia
See all Python Developers in Indonesia

Portfolio

Portfolio

12432536
EDA Video Game Stocks
The analysis utilizes stock market data of the top 10 video game companies to explore stock price trends, trading volume distributions, and seasonal variations. Descriptive statistics and correlation analysis were performed to understand relationships between key variables such as Open, High, Low, Close prices, and Volume. Visualizations reveal the stock price movements over time for each company, highlighting significant fluctuations and trends. A linear regression model was built to predict closing stock prices, with the performance evaluated using Mean Squared Error. Seasonal trends were analyzed for each company, showcasing monthly variations in average stock prices. This analysis provides valuable insights into the behavior of stock prices in the video game industry, helping investors and stakeholders make informed decisions.
EDA Video Game Stocks
The analysis utilizes stock market data of the top 10 video game companies to explore stock price trends, trading volume distributions, and seasonal variations. Descriptive statistics and correlation analysis were performed to understand relationships between key variables such as Open, High, Low, Close prices, and Volume. Visualizations reveal the stock price movements over time for each company, highlighting significant fluctuations and trends. A linear regression model was built to predict closing stock prices, with the performance evaluated using Mean Squared Error. Seasonal trends were analyzed for each company, showcasing monthly variations in average stock prices. This analysis provides valuable insights into the behavior of stock prices in the video game industry, helping investors and stakeholders make informed decisions.
EDA Video Game Stocks
The analysis utilizes stock market data of the top 10 video game companies to explore stock price trends, trading volume distributions, and seasonal variations. Descriptive statistics and correlation analysis were performed to understand relationships between key variables such as Open, High, Low, Close prices, and Volume. Visualizations reveal the stock price movements over time for each company, highlighting significant fluctuations and trends. A linear regression model was built to predict closing stock prices, with the performance evaluated using Mean Squared Error. Seasonal trends were analyzed for each company, showcasing monthly variations in average stock prices. This analysis provides valuable insights into the behavior of stock prices in the video game industry, helping investors and stakeholders make informed decisions.
EDA Video Game Stocks
The analysis utilizes stock market data of the top 10 video game companies to explore stock price trends, trading volume distributions, and seasonal variations. Descriptive statistics and correlation analysis were performed to understand relationships between key variables such as Open, High, Low, Close prices, and Volume. Visualizations reveal the stock price movements over time for each company, highlighting significant fluctuations and trends. A linear regression model was built to predict closing stock prices, with the performance evaluated using Mean Squared Error. Seasonal trends were analyzed for each company, showcasing monthly variations in average stock prices. This analysis provides valuable insights into the behavior of stock prices in the video game industry, helping investors and stakeholders make informed decisions.
EDA Video Game Stocks
The analysis utilizes stock market data of the top 10 video game companies to explore stock price trends, trading volume distributions, and seasonal variations. Descriptive statistics and correlation analysis were performed to understand relationships between key variables such as Open, High, Low, Close prices, and Volume. Visualizations reveal the stock price movements over time for each company, highlighting significant fluctuations and trends. A linear regression model was built to predict closing stock prices, with the performance evaluated using Mean Squared Error. Seasonal trends were analyzed for each company, showcasing monthly variations in average stock prices. This analysis provides valuable insights into the behavior of stock prices in the video game industry, helping investors and stakeholders make informed decisions.
EDA Video Game Stocks
The analysis utilizes stock market data of the top 10 video game companies to explore stock price trends, trading volume distributions, and seasonal variations. Descriptive statistics and correlation analysis were performed to understand relationships between key variables such as Open, High, Low, Close prices, and Volume. Visualizations reveal the stock price movements over time for each company, highlighting significant fluctuations and trends. A linear regression model was built to predict closing stock prices, with the performance evaluated using Mean Squared Error. Seasonal trends were analyzed for each company, showcasing monthly variations in average stock prices. This analysis provides valuable insights into the behavior of stock prices in the video game industry, helping investors and stakeholders make informed decisions.
EDA Video Game Stocks
12432519
GROUP_DATA_ANALYSIS_MasihPemula
Exploring Data-Driven Insights for Business Optimization In my latest project, I analyzed and built predictive models using real-world datasets to provide actionable insights for both the agricultural and financial industries. The project is divided into two parts: a detailed sales analysis for an agricultural supplies company and a credit card fraud detection model designed to enhance transaction security in the financial sector. Part A: Seed and Fertilizer Sales Analysis This analysis examines 1,000 sales records from an agricultural supplies company, focusing on total annual sales per product, average sales per customer, and identifying the top 3 sales representatives. Data visualizations reveal seasonal trends and performance metrics, offering insights to optimize future sales strategies. Part B: Credit Card Fraud Detection This project involves building models to predict credit card billing amounts, cluster customers based on their transaction behaviors, and detect fraudulent ac
GROUP_DATA_ANALYSIS_MasihPemula
Exploring Data-Driven Insights for Business Optimization In my latest project, I analyzed and built predictive models using real-world datasets to provide actionable insights for both the agricultural and financial industries. The project is divided into two parts: a detailed sales analysis for an agricultural supplies company and a credit card fraud detection model designed to enhance transaction security in the financial sector. Part A: Seed and Fertilizer Sales Analysis This analysis examines 1,000 sales records from an agricultural supplies company, focusing on total annual sales per product, average sales per customer, and identifying the top 3 sales representatives. Data visualizations reveal seasonal trends and performance metrics, offering insights to optimize future sales strategies. Part B: Credit Card Fraud Detection This project involves building models to predict credit card billing amounts, cluster customers based on their transaction behaviors, and detect fraudulent ac
GROUP_DATA_ANALYSIS_MasihPemula
Exploring Data-Driven Insights for Business Optimization In my latest project, I analyzed and built predictive models using real-world datasets to provide actionable insights for both the agricultural and financial industries. The project is divided into two parts: a detailed sales analysis for an agricultural supplies company and a credit card fraud detection model designed to enhance transaction security in the financial sector. Part A: Seed and Fertilizer Sales Analysis This analysis examines 1,000 sales records from an agricultural supplies company, focusing on total annual sales per product, average sales per customer, and identifying the top 3 sales representatives. Data visualizations reveal seasonal trends and performance metrics, offering insights to optimize future sales strategies. Part B: Credit Card Fraud Detection This project involves building models to predict credit card billing amounts, cluster customers based on their transaction behaviors, and detect fraudulent ac
GROUP_DATA_ANALYSIS_MasihPemula
Exploring Data-Driven Insights for Business Optimization In my latest project, I analyzed and built predictive models using real-world datasets to provide actionable insights for both the agricultural and financial industries. The project is divided into two parts: a detailed sales analysis for an agricultural supplies company and a credit card fraud detection model designed to enhance transaction security in the financial sector. Part A: Seed and Fertilizer Sales Analysis This analysis examines 1,000 sales records from an agricultural supplies company, focusing on total annual sales per product, average sales per customer, and identifying the top 3 sales representatives. Data visualizations reveal seasonal trends and performance metrics, offering insights to optimize future sales strategies. Part B: Credit Card Fraud Detection This project involves building models to predict credit card billing amounts, cluster customers based on their transaction behaviors, and detect fraudulent ac
GROUP_DATA_ANALYSIS_MasihPemula
Exploring Data-Driven Insights for Business Optimization In my latest project, I analyzed and built predictive models using real-world datasets to provide actionable insights for both the agricultural and financial industries. The project is divided into two parts: a detailed sales analysis for an agricultural supplies company and a credit card fraud detection model designed to enhance transaction security in the financial sector. Part A: Seed and Fertilizer Sales Analysis This analysis examines 1,000 sales records from an agricultural supplies company, focusing on total annual sales per product, average sales per customer, and identifying the top 3 sales representatives. Data visualizations reveal seasonal trends and performance metrics, offering insights to optimize future sales strategies. Part B: Credit Card Fraud Detection This project involves building models to predict credit card billing amounts, cluster customers based on their transaction behaviors, and detect fraudulent ac
GROUP_DATA_ANALYSIS_MasihPemula
12432536
EDA Video Game Stocks
The analysis utilizes stock market data of the top 10 video game companies to explore stock price trends, trading volume distributions, and seasonal variations. Descriptive statistics and correlation analysis were performed to understand relationships between key variables such as Open, High, Low, Close prices, and Volume. Visualizations reveal the stock price movements over time for each company, highlighting significant fluctuations and trends. A linear regression model was built to predict closing stock prices, with the performance evaluated using Mean Squared Error. Seasonal trends were analyzed for each company, showcasing monthly variations in average stock prices. This analysis provides valuable insights into the behavior of stock prices in the video game industry, helping investors and stakeholders make informed decisions.
EDA Video Game Stocks
The analysis utilizes stock market data of the top 10 video game companies to explore stock price trends, trading volume distributions, and seasonal variations. Descriptive statistics and correlation analysis were performed to understand relationships between key variables such as Open, High, Low, Close prices, and Volume. Visualizations reveal the stock price movements over time for each company, highlighting significant fluctuations and trends. A linear regression model was built to predict closing stock prices, with the performance evaluated using Mean Squared Error. Seasonal trends were analyzed for each company, showcasing monthly variations in average stock prices. This analysis provides valuable insights into the behavior of stock prices in the video game industry, helping investors and stakeholders make informed decisions.
EDA Video Game Stocks
The analysis utilizes stock market data of the top 10 video game companies to explore stock price trends, trading volume distributions, and seasonal variations. Descriptive statistics and correlation analysis were performed to understand relationships between key variables such as Open, High, Low, Close prices, and Volume. Visualizations reveal the stock price movements over time for each company, highlighting significant fluctuations and trends. A linear regression model was built to predict closing stock prices, with the performance evaluated using Mean Squared Error. Seasonal trends were analyzed for each company, showcasing monthly variations in average stock prices. This analysis provides valuable insights into the behavior of stock prices in the video game industry, helping investors and stakeholders make informed decisions.
EDA Video Game Stocks
The analysis utilizes stock market data of the top 10 video game companies to explore stock price trends, trading volume distributions, and seasonal variations. Descriptive statistics and correlation analysis were performed to understand relationships between key variables such as Open, High, Low, Close prices, and Volume. Visualizations reveal the stock price movements over time for each company, highlighting significant fluctuations and trends. A linear regression model was built to predict closing stock prices, with the performance evaluated using Mean Squared Error. Seasonal trends were analyzed for each company, showcasing monthly variations in average stock prices. This analysis provides valuable insights into the behavior of stock prices in the video game industry, helping investors and stakeholders make informed decisions.
EDA Video Game Stocks
The analysis utilizes stock market data of the top 10 video game companies to explore stock price trends, trading volume distributions, and seasonal variations. Descriptive statistics and correlation analysis were performed to understand relationships between key variables such as Open, High, Low, Close prices, and Volume. Visualizations reveal the stock price movements over time for each company, highlighting significant fluctuations and trends. A linear regression model was built to predict closing stock prices, with the performance evaluated using Mean Squared Error. Seasonal trends were analyzed for each company, showcasing monthly variations in average stock prices. This analysis provides valuable insights into the behavior of stock prices in the video game industry, helping investors and stakeholders make informed decisions.
EDA Video Game Stocks
The analysis utilizes stock market data of the top 10 video game companies to explore stock price trends, trading volume distributions, and seasonal variations. Descriptive statistics and correlation analysis were performed to understand relationships between key variables such as Open, High, Low, Close prices, and Volume. Visualizations reveal the stock price movements over time for each company, highlighting significant fluctuations and trends. A linear regression model was built to predict closing stock prices, with the performance evaluated using Mean Squared Error. Seasonal trends were analyzed for each company, showcasing monthly variations in average stock prices. This analysis provides valuable insights into the behavior of stock prices in the video game industry, helping investors and stakeholders make informed decisions.
EDA Video Game Stocks
12432519
GROUP_DATA_ANALYSIS_MasihPemula
Exploring Data-Driven Insights for Business Optimization In my latest project, I analyzed and built predictive models using real-world datasets to provide actionable insights for both the agricultural and financial industries. The project is divided into two parts: a detailed sales analysis for an agricultural supplies company and a credit card fraud detection model designed to enhance transaction security in the financial sector. Part A: Seed and Fertilizer Sales Analysis This analysis examines 1,000 sales records from an agricultural supplies company, focusing on total annual sales per product, average sales per customer, and identifying the top 3 sales representatives. Data visualizations reveal seasonal trends and performance metrics, offering insights to optimize future sales strategies. Part B: Credit Card Fraud Detection This project involves building models to predict credit card billing amounts, cluster customers based on their transaction behaviors, and detect fraudulent ac
GROUP_DATA_ANALYSIS_MasihPemula
Exploring Data-Driven Insights for Business Optimization In my latest project, I analyzed and built predictive models using real-world datasets to provide actionable insights for both the agricultural and financial industries. The project is divided into two parts: a detailed sales analysis for an agricultural supplies company and a credit card fraud detection model designed to enhance transaction security in the financial sector. Part A: Seed and Fertilizer Sales Analysis This analysis examines 1,000 sales records from an agricultural supplies company, focusing on total annual sales per product, average sales per customer, and identifying the top 3 sales representatives. Data visualizations reveal seasonal trends and performance metrics, offering insights to optimize future sales strategies. Part B: Credit Card Fraud Detection This project involves building models to predict credit card billing amounts, cluster customers based on their transaction behaviors, and detect fraudulent ac
GROUP_DATA_ANALYSIS_MasihPemula
Exploring Data-Driven Insights for Business Optimization In my latest project, I analyzed and built predictive models using real-world datasets to provide actionable insights for both the agricultural and financial industries. The project is divided into two parts: a detailed sales analysis for an agricultural supplies company and a credit card fraud detection model designed to enhance transaction security in the financial sector. Part A: Seed and Fertilizer Sales Analysis This analysis examines 1,000 sales records from an agricultural supplies company, focusing on total annual sales per product, average sales per customer, and identifying the top 3 sales representatives. Data visualizations reveal seasonal trends and performance metrics, offering insights to optimize future sales strategies. Part B: Credit Card Fraud Detection This project involves building models to predict credit card billing amounts, cluster customers based on their transaction behaviors, and detect fraudulent ac
GROUP_DATA_ANALYSIS_MasihPemula
Exploring Data-Driven Insights for Business Optimization In my latest project, I analyzed and built predictive models using real-world datasets to provide actionable insights for both the agricultural and financial industries. The project is divided into two parts: a detailed sales analysis for an agricultural supplies company and a credit card fraud detection model designed to enhance transaction security in the financial sector. Part A: Seed and Fertilizer Sales Analysis This analysis examines 1,000 sales records from an agricultural supplies company, focusing on total annual sales per product, average sales per customer, and identifying the top 3 sales representatives. Data visualizations reveal seasonal trends and performance metrics, offering insights to optimize future sales strategies. Part B: Credit Card Fraud Detection This project involves building models to predict credit card billing amounts, cluster customers based on their transaction behaviors, and detect fraudulent ac
GROUP_DATA_ANALYSIS_MasihPemula
Exploring Data-Driven Insights for Business Optimization In my latest project, I analyzed and built predictive models using real-world datasets to provide actionable insights for both the agricultural and financial industries. The project is divided into two parts: a detailed sales analysis for an agricultural supplies company and a credit card fraud detection model designed to enhance transaction security in the financial sector. Part A: Seed and Fertilizer Sales Analysis This analysis examines 1,000 sales records from an agricultural supplies company, focusing on total annual sales per product, average sales per customer, and identifying the top 3 sales representatives. Data visualizations reveal seasonal trends and performance metrics, offering insights to optimize future sales strategies. Part B: Credit Card Fraud Detection This project involves building models to predict credit card billing amounts, cluster customers based on their transaction behaviors, and detect fraudulent ac
GROUP_DATA_ANALYSIS_MasihPemula
Changes saved
5.0 · 1 Review
Reviews
E
Looking for Help Annotating Construction Plans • €19 EUR
he got involved beyond all expectations even though I didn't award him (I still have everything to do, I know)
P G.
@egotrippes
•
Flag of
Paris, France
•
4 days ago
Share
Invite sent successfully!
Thanks! We’ve emailed you a link to claim your free credit.
Something went wrong while sending your email. Please try again.
Freelancer logo Jamaica / English
Help & Support
Freelancer
Categories Projects Contests Freelancers Enterprise AI Development Membership Preferred Freelancer Program Project Management Local Jobs Photo Anywhere Showcase API for Developers Get Verified Desktop App
About
About us How it Works Security Investor Sitemap Stories News Team Awards Press Releases Careers
Terms
Privacy Policy Terms and Conditions Copyright Policy Code of Conduct Fees and Charges
Partners
Escrow.com Loadshift Warrior Forum
Apps
Apple App Store logoGoogle Play logo
Copy to clipboard failed, please try again after adjusting your permissions.
Copied to clipboard.
Registered Users Total Jobs Posted
Freelancer ® is a registered Trademark of Freelancer Technology Pty Limited (ACN 142 189 759)
Copyright © 2025 Freelancer Technology Pty Limited (ACN 142 189 759)
Loading preview
Permission granted for Geolocation.
Your login session has expired and you have been logged out. Please log in again.