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Brukeravatar

Dimas P.

@PashaAkrilian

5,0
1
0,6
0,6
100%

Data Scientist

$50 USD / time
・
s flagg
Indonesia (5:44 a.m.)
・
Ble med desember 11, 2024
$50 USD / time
・
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.
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100 %
Freelancer Python Developers Indonesia
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Portefølje

Portefølje

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
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5.0 · 1 Review
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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
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s flagg
Paris, France
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