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$5 USD / time
Flag for PAKISTAN
lahore, punjab, pakistan
$5 USD / time
Det er i øjeblikket 7:10 AM her
Tilmeldt december 19, 2020
0 Anbefalinger

Samiullah A.

@Sami276

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$5 USD / time
Flag for PAKISTAN
lahore, punjab, pakistan
$5 USD / time
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R/Python Developer | Data Science, Web and AI

I am a skilled software developer with approximately 3 years of experience in building applications using R and Python. My expertise lies in the following areas: 1. Dashboard/Web App Development: I specialize in developing interactive dashboards and web applications using RShiny. I have successfully created intuitive and visually appealing user interfaces to present complex data and analytics. 2. Data Science, Analytics, and Machine Learning: I have a strong background in data science and analytics, utilizing both R and Python. I am proficient in various data manipulation, exploration, and visualization techniques. Additionally, I have experience in applying machine learning algorithms to solve real-world problems and generate valuable insights. 3. Web Scraping and Automation: I am skilled in web scraping techniques using R and Python, particularly utilizing Selenium for automated data extraction. This expertise allows me to gather data from various online sources efficiently and effectively. 4. Custom Debugging, Technical Support, and Tutoring: I possess the ability to provide custom debugging and technical support for R and Python applications. I am experienced in troubleshooting code, identifying and resolving errors, and optimizing performance. Furthermore, I have a passion for teaching and mentoring, offering tutoring services to help individuals enhance their coding skills in R and Python. With keen attention to detail and a commitment to delivering error-free solutions, I consistently strive for excellence in my work. My academic background includes relevant education in the field, further complementing my practical experience. Thank you for considering my skills and experience. I am confident that my expertise will be an asset in meeting your project requirements.
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Portfolio-mappe

11203994
This project involves utilizing data analytics techniques within the R framework to analyze global video game sales trends based on various factors such as genre, platform, ratings, and origin. The project progresses through several phases, commencing with data pre-processing and cleaning to address missing values, potential outliers, and maintain data structure. Subsequently, data visualization is performed using the Highchart package in R to present interactive bar, line, and pie charts, enabling a comprehensive understanding of the dataset. Finally, the data is scaled for training and testing, feeding into machine learning models, including SVM, decision trees, and random forests, to generate model evaluation metrics such as accuracy and stat score parameters. The respective confusion matrices are generated for each model to assess their performance.
Video Game Data Analysis | R
This project involves utilizing data analytics techniques within the R framework to analyze global video game sales trends based on various factors such as genre, platform, ratings, and origin. The project progresses through several phases, commencing with data pre-processing and cleaning to address missing values, potential outliers, and maintain data structure. Subsequently, data visualization is performed using the Highchart package in R to present interactive bar, line, and pie charts, enabling a comprehensive understanding of the dataset. Finally, the data is scaled for training and testing, feeding into machine learning models, including SVM, decision trees, and random forests, to generate model evaluation metrics such as accuracy and stat score parameters. The respective confusion matrices are generated for each model to assess their performance.
Video Game Data Analysis | R
This project involves utilizing data analytics techniques within the R framework to analyze global video game sales trends based on various factors such as genre, platform, ratings, and origin. The project progresses through several phases, commencing with data pre-processing and cleaning to address missing values, potential outliers, and maintain data structure. Subsequently, data visualization is performed using the Highchart package in R to present interactive bar, line, and pie charts, enabling a comprehensive understanding of the dataset. Finally, the data is scaled for training and testing, feeding into machine learning models, including SVM, decision trees, and random forests, to generate model evaluation metrics such as accuracy and stat score parameters. The respective confusion matrices are generated for each model to assess their performance.
Video Game Data Analysis | R
This project involves utilizing data analytics techniques within the R framework to analyze global video game sales trends based on various factors such as genre, platform, ratings, and origin. The project progresses through several phases, commencing with data pre-processing and cleaning to address missing values, potential outliers, and maintain data structure. Subsequently, data visualization is performed using the Highchart package in R to present interactive bar, line, and pie charts, enabling a comprehensive understanding of the dataset. Finally, the data is scaled for training and testing, feeding into machine learning models, including SVM, decision trees, and random forests, to generate model evaluation metrics such as accuracy and stat score parameters. The respective confusion matrices are generated for each model to assess their performance.
Video Game Data Analysis | R
This project involves utilizing data analytics techniques within the R framework to analyze global video game sales trends based on various factors such as genre, platform, ratings, and origin. The project progresses through several phases, commencing with data pre-processing and cleaning to address missing values, potential outliers, and maintain data structure. Subsequently, data visualization is performed using the Highchart package in R to present interactive bar, line, and pie charts, enabling a comprehensive understanding of the dataset. Finally, the data is scaled for training and testing, feeding into machine learning models, including SVM, decision trees, and random forests, to generate model evaluation metrics such as accuracy and stat score parameters. The respective confusion matrices are generated for each model to assess their performance.
Video Game Data Analysis | R
11189641
The objective of this project is to determine gender based on the acoustic characteristics of males and females. The project commences with data exploration and pre-processing, during which the data is visually analyzed in both tabular and graphical formats to gain insights and identify any missing values or outliers. Potential outliers are detected using the z-score method provided by the Scipy library. Subsequently, a comprehensive analysis of the features is performed, resulting in a reduction based on their rankings. Finally, the dataset is fed into machine learning models, and their metrics are assessed to evaluate accuracy and reliability.
Gender Detection based on Acoustics | Python
The objective of this project is to determine gender based on the acoustic characteristics of males and females. The project commences with data exploration and pre-processing, during which the data is visually analyzed in both tabular and graphical formats to gain insights and identify any missing values or outliers. Potential outliers are detected using the z-score method provided by the Scipy library. Subsequently, a comprehensive analysis of the features is performed, resulting in a reduction based on their rankings. Finally, the dataset is fed into machine learning models, and their metrics are assessed to evaluate accuracy and reliability.
Gender Detection based on Acoustics | Python
The objective of this project is to determine gender based on the acoustic characteristics of males and females. The project commences with data exploration and pre-processing, during which the data is visually analyzed in both tabular and graphical formats to gain insights and identify any missing values or outliers. Potential outliers are detected using the z-score method provided by the Scipy library. Subsequently, a comprehensive analysis of the features is performed, resulting in a reduction based on their rankings. Finally, the dataset is fed into machine learning models, and their metrics are assessed to evaluate accuracy and reliability.
Gender Detection based on Acoustics | Python
The objective of this project is to determine gender based on the acoustic characteristics of males and females. The project commences with data exploration and pre-processing, during which the data is visually analyzed in both tabular and graphical formats to gain insights and identify any missing values or outliers. Potential outliers are detected using the z-score method provided by the Scipy library. Subsequently, a comprehensive analysis of the features is performed, resulting in a reduction based on their rankings. Finally, the dataset is fed into machine learning models, and their metrics are assessed to evaluate accuracy and reliability.
Gender Detection based on Acoustics | Python
The objective of this project is to determine gender based on the acoustic characteristics of males and females. The project commences with data exploration and pre-processing, during which the data is visually analyzed in both tabular and graphical formats to gain insights and identify any missing values or outliers. Potential outliers are detected using the z-score method provided by the Scipy library. Subsequently, a comprehensive analysis of the features is performed, resulting in a reduction based on their rankings. Finally, the dataset is fed into machine learning models, and their metrics are assessed to evaluate accuracy and reliability.
Gender Detection based on Acoustics | Python
11161622
This project presents a comprehensive approach to analyzing the Covid-19 dataset. It starts by importing necessary libraries and examining the characteristics of various features in the dataset. Next, the data is manipulated to achieve the desired data types and structure. This involves removing unnecessary rows, labeling the data, and converting strings into numeric and binary classification factors. Key roles are played by libraries such as pandas and numpy. The pre-processed data is then visualized to extract meaningful insights from the dataset. Matplotlib and seaborn libraries are employed, offering various methods for displaying figures with numerical and categorical data, including histograms, barplots, and correlation heatmaps. Subsequently, the prediction phase begins, where the data is balanced using the imblearn package. Different machine learning models like KNN and Decision are utilized to forecast future values.
Covid-19 Analysis | Python (Jupyter Notebook)
This project presents a comprehensive approach to analyzing the Covid-19 dataset. It starts by importing necessary libraries and examining the characteristics of various features in the dataset. Next, the data is manipulated to achieve the desired data types and structure. This involves removing unnecessary rows, labeling the data, and converting strings into numeric and binary classification factors. Key roles are played by libraries such as pandas and numpy. The pre-processed data is then visualized to extract meaningful insights from the dataset. Matplotlib and seaborn libraries are employed, offering various methods for displaying figures with numerical and categorical data, including histograms, barplots, and correlation heatmaps. Subsequently, the prediction phase begins, where the data is balanced using the imblearn package. Different machine learning models like KNN and Decision are utilized to forecast future values.
Covid-19 Analysis | Python (Jupyter Notebook)
This project presents a comprehensive approach to analyzing the Covid-19 dataset. It starts by importing necessary libraries and examining the characteristics of various features in the dataset. Next, the data is manipulated to achieve the desired data types and structure. This involves removing unnecessary rows, labeling the data, and converting strings into numeric and binary classification factors. Key roles are played by libraries such as pandas and numpy. The pre-processed data is then visualized to extract meaningful insights from the dataset. Matplotlib and seaborn libraries are employed, offering various methods for displaying figures with numerical and categorical data, including histograms, barplots, and correlation heatmaps. Subsequently, the prediction phase begins, where the data is balanced using the imblearn package. Different machine learning models like KNN and Decision are utilized to forecast future values.
Covid-19 Analysis | Python (Jupyter Notebook)
This project presents a comprehensive approach to analyzing the Covid-19 dataset. It starts by importing necessary libraries and examining the characteristics of various features in the dataset. Next, the data is manipulated to achieve the desired data types and structure. This involves removing unnecessary rows, labeling the data, and converting strings into numeric and binary classification factors. Key roles are played by libraries such as pandas and numpy. The pre-processed data is then visualized to extract meaningful insights from the dataset. Matplotlib and seaborn libraries are employed, offering various methods for displaying figures with numerical and categorical data, including histograms, barplots, and correlation heatmaps. Subsequently, the prediction phase begins, where the data is balanced using the imblearn package. Different machine learning models like KNN and Decision are utilized to forecast future values.
Covid-19 Analysis | Python (Jupyter Notebook)
This project presents a comprehensive approach to analyzing the Covid-19 dataset. It starts by importing necessary libraries and examining the characteristics of various features in the dataset. Next, the data is manipulated to achieve the desired data types and structure. This involves removing unnecessary rows, labeling the data, and converting strings into numeric and binary classification factors. Key roles are played by libraries such as pandas and numpy. The pre-processed data is then visualized to extract meaningful insights from the dataset. Matplotlib and seaborn libraries are employed, offering various methods for displaying figures with numerical and categorical data, including histograms, barplots, and correlation heatmaps. Subsequently, the prediction phase begins, where the data is balanced using the imblearn package. Different machine learning models like KNN and Decision are utilized to forecast future values.
Covid-19 Analysis | Python (Jupyter Notebook)
11150144
This project implements a Warehouse Manager application using object-oriented programming in Python3. It defines classes for customers, parts, and exceptions. Customers have IDs, names, and different discount rates. Parts have IDs, names, prices, and quantities. The application can read customer and part data from files and display them. It handles operations like replenishing parts, supplying parts to customers, and assembling parts. The WarehouseManagerUI class serves as the controller, interacting with users through a menu-driven interface. It validates user input and performs corresponding actions. The application demonstrates basic functionality for managing parts and customers in a warehouse setting using the object-oriented approach (OOP) of Application/Software Development
Parts Inventory application using Python3 OOP
This project implements a Warehouse Manager application using object-oriented programming in Python3. It defines classes for customers, parts, and exceptions. Customers have IDs, names, and different discount rates. Parts have IDs, names, prices, and quantities. The application can read customer and part data from files and display them. It handles operations like replenishing parts, supplying parts to customers, and assembling parts. The WarehouseManagerUI class serves as the controller, interacting with users through a menu-driven interface. It validates user input and performs corresponding actions. The application demonstrates basic functionality for managing parts and customers in a warehouse setting using the object-oriented approach (OOP) of Application/Software Development
Parts Inventory application using Python3 OOP
11145096
For this particular project, I employed the utilization of Selenium, a robust framework, to automate the functionality of a game website called Sweet Booty's Bakery. The primary objective of the game revolves around clicking on a cookie to accumulate points, subsequently redeeming them to acquire a specific assortment of bakery items. To accomplish this, an automated process was implemented wherein the act of clicking on the cookies was executed at a frequency of once per second, resulting in the accumulation of 1000 points. Once this milestone was achieved, the accumulated points were then utilized to obtain a delectable chocolate cake from the bakery's offerings.
Web Automation using Python Selenium
11136504
Intro: The Common Delivery Points (CDP) refer to the interconnected network of grids and substations, outlining the transfer of electricity from power plants to households. These grid points play a crucial role in monitoring the energy flow through the system and adjusting voltage levels to minimize losses. Additionally, they track the amount of energy present in specific regions to regulate the energy distribution. About the App: The application includes a login page that redirects users to the main page upon successful verification. The main page comprises two subsections, allowing users to choose the timeline and CDP station for viewing information, presented in color-coded boxes. The application also includes a map, enabling users to locate and study the energy distribution network. Finally, time-series analysis and heatmaps display the daily and hourly trends of energy consumption for various weekdays.
Common Delivery Points (CDP) Web Application | RShiny
Intro: The Common Delivery Points (CDP) refer to the interconnected network of grids and substations, outlining the transfer of electricity from power plants to households. These grid points play a crucial role in monitoring the energy flow through the system and adjusting voltage levels to minimize losses. Additionally, they track the amount of energy present in specific regions to regulate the energy distribution. About the App: The application includes a login page that redirects users to the main page upon successful verification. The main page comprises two subsections, allowing users to choose the timeline and CDP station for viewing information, presented in color-coded boxes. The application also includes a map, enabling users to locate and study the energy distribution network. Finally, time-series analysis and heatmaps display the daily and hourly trends of energy consumption for various weekdays.
Common Delivery Points (CDP) Web Application | RShiny
Intro: The Common Delivery Points (CDP) refer to the interconnected network of grids and substations, outlining the transfer of electricity from power plants to households. These grid points play a crucial role in monitoring the energy flow through the system and adjusting voltage levels to minimize losses. Additionally, they track the amount of energy present in specific regions to regulate the energy distribution. About the App: The application includes a login page that redirects users to the main page upon successful verification. The main page comprises two subsections, allowing users to choose the timeline and CDP station for viewing information, presented in color-coded boxes. The application also includes a map, enabling users to locate and study the energy distribution network. Finally, time-series analysis and heatmaps display the daily and hourly trends of energy consumption for various weekdays.
Common Delivery Points (CDP) Web Application | RShiny
Intro: The Common Delivery Points (CDP) refer to the interconnected network of grids and substations, outlining the transfer of electricity from power plants to households. These grid points play a crucial role in monitoring the energy flow through the system and adjusting voltage levels to minimize losses. Additionally, they track the amount of energy present in specific regions to regulate the energy distribution. About the App: The application includes a login page that redirects users to the main page upon successful verification. The main page comprises two subsections, allowing users to choose the timeline and CDP station for viewing information, presented in color-coded boxes. The application also includes a map, enabling users to locate and study the energy distribution network. Finally, time-series analysis and heatmaps display the daily and hourly trends of energy consumption for various weekdays.
Common Delivery Points (CDP) Web Application | RShiny
Intro: The Common Delivery Points (CDP) refer to the interconnected network of grids and substations, outlining the transfer of electricity from power plants to households. These grid points play a crucial role in monitoring the energy flow through the system and adjusting voltage levels to minimize losses. Additionally, they track the amount of energy present in specific regions to regulate the energy distribution. About the App: The application includes a login page that redirects users to the main page upon successful verification. The main page comprises two subsections, allowing users to choose the timeline and CDP station for viewing information, presented in color-coded boxes. The application also includes a map, enabling users to locate and study the energy distribution network. Finally, time-series analysis and heatmaps display the daily and hourly trends of energy consumption for various weekdays.
Common Delivery Points (CDP) Web Application | RShiny
Intro: The Common Delivery Points (CDP) refer to the interconnected network of grids and substations, outlining the transfer of electricity from power plants to households. These grid points play a crucial role in monitoring the energy flow through the system and adjusting voltage levels to minimize losses. Additionally, they track the amount of energy present in specific regions to regulate the energy distribution. About the App: The application includes a login page that redirects users to the main page upon successful verification. The main page comprises two subsections, allowing users to choose the timeline and CDP station for viewing information, presented in color-coded boxes. The application also includes a map, enabling users to locate and study the energy distribution network. Finally, time-series analysis and heatmaps display the daily and hourly trends of energy consumption for various weekdays.
Common Delivery Points (CDP) Web Application | RShiny
11203994
This project involves utilizing data analytics techniques within the R framework to analyze global video game sales trends based on various factors such as genre, platform, ratings, and origin. The project progresses through several phases, commencing with data pre-processing and cleaning to address missing values, potential outliers, and maintain data structure. Subsequently, data visualization is performed using the Highchart package in R to present interactive bar, line, and pie charts, enabling a comprehensive understanding of the dataset. Finally, the data is scaled for training and testing, feeding into machine learning models, including SVM, decision trees, and random forests, to generate model evaluation metrics such as accuracy and stat score parameters. The respective confusion matrices are generated for each model to assess their performance.
Video Game Data Analysis | R
This project involves utilizing data analytics techniques within the R framework to analyze global video game sales trends based on various factors such as genre, platform, ratings, and origin. The project progresses through several phases, commencing with data pre-processing and cleaning to address missing values, potential outliers, and maintain data structure. Subsequently, data visualization is performed using the Highchart package in R to present interactive bar, line, and pie charts, enabling a comprehensive understanding of the dataset. Finally, the data is scaled for training and testing, feeding into machine learning models, including SVM, decision trees, and random forests, to generate model evaluation metrics such as accuracy and stat score parameters. The respective confusion matrices are generated for each model to assess their performance.
Video Game Data Analysis | R
This project involves utilizing data analytics techniques within the R framework to analyze global video game sales trends based on various factors such as genre, platform, ratings, and origin. The project progresses through several phases, commencing with data pre-processing and cleaning to address missing values, potential outliers, and maintain data structure. Subsequently, data visualization is performed using the Highchart package in R to present interactive bar, line, and pie charts, enabling a comprehensive understanding of the dataset. Finally, the data is scaled for training and testing, feeding into machine learning models, including SVM, decision trees, and random forests, to generate model evaluation metrics such as accuracy and stat score parameters. The respective confusion matrices are generated for each model to assess their performance.
Video Game Data Analysis | R
This project involves utilizing data analytics techniques within the R framework to analyze global video game sales trends based on various factors such as genre, platform, ratings, and origin. The project progresses through several phases, commencing with data pre-processing and cleaning to address missing values, potential outliers, and maintain data structure. Subsequently, data visualization is performed using the Highchart package in R to present interactive bar, line, and pie charts, enabling a comprehensive understanding of the dataset. Finally, the data is scaled for training and testing, feeding into machine learning models, including SVM, decision trees, and random forests, to generate model evaluation metrics such as accuracy and stat score parameters. The respective confusion matrices are generated for each model to assess their performance.
Video Game Data Analysis | R
This project involves utilizing data analytics techniques within the R framework to analyze global video game sales trends based on various factors such as genre, platform, ratings, and origin. The project progresses through several phases, commencing with data pre-processing and cleaning to address missing values, potential outliers, and maintain data structure. Subsequently, data visualization is performed using the Highchart package in R to present interactive bar, line, and pie charts, enabling a comprehensive understanding of the dataset. Finally, the data is scaled for training and testing, feeding into machine learning models, including SVM, decision trees, and random forests, to generate model evaluation metrics such as accuracy and stat score parameters. The respective confusion matrices are generated for each model to assess their performance.
Video Game Data Analysis | R
11189641
The objective of this project is to determine gender based on the acoustic characteristics of males and females. The project commences with data exploration and pre-processing, during which the data is visually analyzed in both tabular and graphical formats to gain insights and identify any missing values or outliers. Potential outliers are detected using the z-score method provided by the Scipy library. Subsequently, a comprehensive analysis of the features is performed, resulting in a reduction based on their rankings. Finally, the dataset is fed into machine learning models, and their metrics are assessed to evaluate accuracy and reliability.
Gender Detection based on Acoustics | Python
The objective of this project is to determine gender based on the acoustic characteristics of males and females. The project commences with data exploration and pre-processing, during which the data is visually analyzed in both tabular and graphical formats to gain insights and identify any missing values or outliers. Potential outliers are detected using the z-score method provided by the Scipy library. Subsequently, a comprehensive analysis of the features is performed, resulting in a reduction based on their rankings. Finally, the dataset is fed into machine learning models, and their metrics are assessed to evaluate accuracy and reliability.
Gender Detection based on Acoustics | Python
The objective of this project is to determine gender based on the acoustic characteristics of males and females. The project commences with data exploration and pre-processing, during which the data is visually analyzed in both tabular and graphical formats to gain insights and identify any missing values or outliers. Potential outliers are detected using the z-score method provided by the Scipy library. Subsequently, a comprehensive analysis of the features is performed, resulting in a reduction based on their rankings. Finally, the dataset is fed into machine learning models, and their metrics are assessed to evaluate accuracy and reliability.
Gender Detection based on Acoustics | Python
The objective of this project is to determine gender based on the acoustic characteristics of males and females. The project commences with data exploration and pre-processing, during which the data is visually analyzed in both tabular and graphical formats to gain insights and identify any missing values or outliers. Potential outliers are detected using the z-score method provided by the Scipy library. Subsequently, a comprehensive analysis of the features is performed, resulting in a reduction based on their rankings. Finally, the dataset is fed into machine learning models, and their metrics are assessed to evaluate accuracy and reliability.
Gender Detection based on Acoustics | Python
The objective of this project is to determine gender based on the acoustic characteristics of males and females. The project commences with data exploration and pre-processing, during which the data is visually analyzed in both tabular and graphical formats to gain insights and identify any missing values or outliers. Potential outliers are detected using the z-score method provided by the Scipy library. Subsequently, a comprehensive analysis of the features is performed, resulting in a reduction based on their rankings. Finally, the dataset is fed into machine learning models, and their metrics are assessed to evaluate accuracy and reliability.
Gender Detection based on Acoustics | Python
11161622
This project presents a comprehensive approach to analyzing the Covid-19 dataset. It starts by importing necessary libraries and examining the characteristics of various features in the dataset. Next, the data is manipulated to achieve the desired data types and structure. This involves removing unnecessary rows, labeling the data, and converting strings into numeric and binary classification factors. Key roles are played by libraries such as pandas and numpy. The pre-processed data is then visualized to extract meaningful insights from the dataset. Matplotlib and seaborn libraries are employed, offering various methods for displaying figures with numerical and categorical data, including histograms, barplots, and correlation heatmaps. Subsequently, the prediction phase begins, where the data is balanced using the imblearn package. Different machine learning models like KNN and Decision are utilized to forecast future values.
Covid-19 Analysis | Python (Jupyter Notebook)
This project presents a comprehensive approach to analyzing the Covid-19 dataset. It starts by importing necessary libraries and examining the characteristics of various features in the dataset. Next, the data is manipulated to achieve the desired data types and structure. This involves removing unnecessary rows, labeling the data, and converting strings into numeric and binary classification factors. Key roles are played by libraries such as pandas and numpy. The pre-processed data is then visualized to extract meaningful insights from the dataset. Matplotlib and seaborn libraries are employed, offering various methods for displaying figures with numerical and categorical data, including histograms, barplots, and correlation heatmaps. Subsequently, the prediction phase begins, where the data is balanced using the imblearn package. Different machine learning models like KNN and Decision are utilized to forecast future values.
Covid-19 Analysis | Python (Jupyter Notebook)
This project presents a comprehensive approach to analyzing the Covid-19 dataset. It starts by importing necessary libraries and examining the characteristics of various features in the dataset. Next, the data is manipulated to achieve the desired data types and structure. This involves removing unnecessary rows, labeling the data, and converting strings into numeric and binary classification factors. Key roles are played by libraries such as pandas and numpy. The pre-processed data is then visualized to extract meaningful insights from the dataset. Matplotlib and seaborn libraries are employed, offering various methods for displaying figures with numerical and categorical data, including histograms, barplots, and correlation heatmaps. Subsequently, the prediction phase begins, where the data is balanced using the imblearn package. Different machine learning models like KNN and Decision are utilized to forecast future values.
Covid-19 Analysis | Python (Jupyter Notebook)
This project presents a comprehensive approach to analyzing the Covid-19 dataset. It starts by importing necessary libraries and examining the characteristics of various features in the dataset. Next, the data is manipulated to achieve the desired data types and structure. This involves removing unnecessary rows, labeling the data, and converting strings into numeric and binary classification factors. Key roles are played by libraries such as pandas and numpy. The pre-processed data is then visualized to extract meaningful insights from the dataset. Matplotlib and seaborn libraries are employed, offering various methods for displaying figures with numerical and categorical data, including histograms, barplots, and correlation heatmaps. Subsequently, the prediction phase begins, where the data is balanced using the imblearn package. Different machine learning models like KNN and Decision are utilized to forecast future values.
Covid-19 Analysis | Python (Jupyter Notebook)
This project presents a comprehensive approach to analyzing the Covid-19 dataset. It starts by importing necessary libraries and examining the characteristics of various features in the dataset. Next, the data is manipulated to achieve the desired data types and structure. This involves removing unnecessary rows, labeling the data, and converting strings into numeric and binary classification factors. Key roles are played by libraries such as pandas and numpy. The pre-processed data is then visualized to extract meaningful insights from the dataset. Matplotlib and seaborn libraries are employed, offering various methods for displaying figures with numerical and categorical data, including histograms, barplots, and correlation heatmaps. Subsequently, the prediction phase begins, where the data is balanced using the imblearn package. Different machine learning models like KNN and Decision are utilized to forecast future values.
Covid-19 Analysis | Python (Jupyter Notebook)
11150144
This project implements a Warehouse Manager application using object-oriented programming in Python3. It defines classes for customers, parts, and exceptions. Customers have IDs, names, and different discount rates. Parts have IDs, names, prices, and quantities. The application can read customer and part data from files and display them. It handles operations like replenishing parts, supplying parts to customers, and assembling parts. The WarehouseManagerUI class serves as the controller, interacting with users through a menu-driven interface. It validates user input and performs corresponding actions. The application demonstrates basic functionality for managing parts and customers in a warehouse setting using the object-oriented approach (OOP) of Application/Software Development
Parts Inventory application using Python3 OOP
This project implements a Warehouse Manager application using object-oriented programming in Python3. It defines classes for customers, parts, and exceptions. Customers have IDs, names, and different discount rates. Parts have IDs, names, prices, and quantities. The application can read customer and part data from files and display them. It handles operations like replenishing parts, supplying parts to customers, and assembling parts. The WarehouseManagerUI class serves as the controller, interacting with users through a menu-driven interface. It validates user input and performs corresponding actions. The application demonstrates basic functionality for managing parts and customers in a warehouse setting using the object-oriented approach (OOP) of Application/Software Development
Parts Inventory application using Python3 OOP
11145096
For this particular project, I employed the utilization of Selenium, a robust framework, to automate the functionality of a game website called Sweet Booty's Bakery. The primary objective of the game revolves around clicking on a cookie to accumulate points, subsequently redeeming them to acquire a specific assortment of bakery items. To accomplish this, an automated process was implemented wherein the act of clicking on the cookies was executed at a frequency of once per second, resulting in the accumulation of 1000 points. Once this milestone was achieved, the accumulated points were then utilized to obtain a delectable chocolate cake from the bakery's offerings.
Web Automation using Python Selenium
11136504
Intro: The Common Delivery Points (CDP) refer to the interconnected network of grids and substations, outlining the transfer of electricity from power plants to households. These grid points play a crucial role in monitoring the energy flow through the system and adjusting voltage levels to minimize losses. Additionally, they track the amount of energy present in specific regions to regulate the energy distribution. About the App: The application includes a login page that redirects users to the main page upon successful verification. The main page comprises two subsections, allowing users to choose the timeline and CDP station for viewing information, presented in color-coded boxes. The application also includes a map, enabling users to locate and study the energy distribution network. Finally, time-series analysis and heatmaps display the daily and hourly trends of energy consumption for various weekdays.
Common Delivery Points (CDP) Web Application | RShiny
Intro: The Common Delivery Points (CDP) refer to the interconnected network of grids and substations, outlining the transfer of electricity from power plants to households. These grid points play a crucial role in monitoring the energy flow through the system and adjusting voltage levels to minimize losses. Additionally, they track the amount of energy present in specific regions to regulate the energy distribution. About the App: The application includes a login page that redirects users to the main page upon successful verification. The main page comprises two subsections, allowing users to choose the timeline and CDP station for viewing information, presented in color-coded boxes. The application also includes a map, enabling users to locate and study the energy distribution network. Finally, time-series analysis and heatmaps display the daily and hourly trends of energy consumption for various weekdays.
Common Delivery Points (CDP) Web Application | RShiny
Intro: The Common Delivery Points (CDP) refer to the interconnected network of grids and substations, outlining the transfer of electricity from power plants to households. These grid points play a crucial role in monitoring the energy flow through the system and adjusting voltage levels to minimize losses. Additionally, they track the amount of energy present in specific regions to regulate the energy distribution. About the App: The application includes a login page that redirects users to the main page upon successful verification. The main page comprises two subsections, allowing users to choose the timeline and CDP station for viewing information, presented in color-coded boxes. The application also includes a map, enabling users to locate and study the energy distribution network. Finally, time-series analysis and heatmaps display the daily and hourly trends of energy consumption for various weekdays.
Common Delivery Points (CDP) Web Application | RShiny
Intro: The Common Delivery Points (CDP) refer to the interconnected network of grids and substations, outlining the transfer of electricity from power plants to households. These grid points play a crucial role in monitoring the energy flow through the system and adjusting voltage levels to minimize losses. Additionally, they track the amount of energy present in specific regions to regulate the energy distribution. About the App: The application includes a login page that redirects users to the main page upon successful verification. The main page comprises two subsections, allowing users to choose the timeline and CDP station for viewing information, presented in color-coded boxes. The application also includes a map, enabling users to locate and study the energy distribution network. Finally, time-series analysis and heatmaps display the daily and hourly trends of energy consumption for various weekdays.
Common Delivery Points (CDP) Web Application | RShiny
Intro: The Common Delivery Points (CDP) refer to the interconnected network of grids and substations, outlining the transfer of electricity from power plants to households. These grid points play a crucial role in monitoring the energy flow through the system and adjusting voltage levels to minimize losses. Additionally, they track the amount of energy present in specific regions to regulate the energy distribution. About the App: The application includes a login page that redirects users to the main page upon successful verification. The main page comprises two subsections, allowing users to choose the timeline and CDP station for viewing information, presented in color-coded boxes. The application also includes a map, enabling users to locate and study the energy distribution network. Finally, time-series analysis and heatmaps display the daily and hourly trends of energy consumption for various weekdays.
Common Delivery Points (CDP) Web Application | RShiny
Intro: The Common Delivery Points (CDP) refer to the interconnected network of grids and substations, outlining the transfer of electricity from power plants to households. These grid points play a crucial role in monitoring the energy flow through the system and adjusting voltage levels to minimize losses. Additionally, they track the amount of energy present in specific regions to regulate the energy distribution. About the App: The application includes a login page that redirects users to the main page upon successful verification. The main page comprises two subsections, allowing users to choose the timeline and CDP station for viewing information, presented in color-coded boxes. The application also includes a map, enabling users to locate and study the energy distribution network. Finally, time-series analysis and heatmaps display the daily and hourly trends of energy consumption for various weekdays.
Common Delivery Points (CDP) Web Application | RShiny

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