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Muhammad W.

@waleed9098

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web scraper fullstack developer web scraping

$15 USD / Hour
・
Flag of
Pakistan (1:22 PM)
・
Joined on April 16, 2024
$15 USD / Hour
・
Welcome to my profile! I am a Computer Engineer specialize in tailoring machine learning solutions across supervised, unsupervised, and reinforcement learning paradigms. With two years of hands-on experience, I offer precise and effective services designed to elevate your business.
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See all PHP Developers in Pakistan

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Portfolio

11948079
Training a data model to predict future soccer match outcomes involves several steps. We'll need to gather relevant data, preprocess it, choose an appropriate model, train it, and then evaluate its performance. Here's a step-by-step guide on how to do this: Step 1: Gather Data We'll need historical data on soccer matches. This can include: Match results (wins, losses, draws) Team statistics (goals scored, goals conceded, possession, shots on target, etc.) Player statistics (goals, assists, injuries, etc.) Venue information (home/away) Team form (recent performance) Step 2: Preprocess Data Clean the data and create features that will help the model learn patterns. This might involve: Handling missing values Normalizing/standardizing numerical features Encoding categorical variables (e.g., team names, venues) Creating aggregate statistics (e.g., average goals per match) Step 3: Choose a Model For predicting match outcomes, we can use classification models such as: Logistic Regression Dec
predict future soccer match
Training a data model to predict future soccer match outcomes involves several steps. We'll need to gather relevant data, preprocess it, choose an appropriate model, train it, and then evaluate its performance. Here's a step-by-step guide on how to do this: Step 1: Gather Data We'll need historical data on soccer matches. This can include: Match results (wins, losses, draws) Team statistics (goals scored, goals conceded, possession, shots on target, etc.) Player statistics (goals, assists, injuries, etc.) Venue information (home/away) Team form (recent performance) Step 2: Preprocess Data Clean the data and create features that will help the model learn patterns. This might involve: Handling missing values Normalizing/standardizing numerical features Encoding categorical variables (e.g., team names, venues) Creating aggregate statistics (e.g., average goals per match) Step 3: Choose a Model For predicting match outcomes, we can use classification models such as: Logistic Regression Dec
predict future soccer match
Training a data model to predict future soccer match outcomes involves several steps. We'll need to gather relevant data, preprocess it, choose an appropriate model, train it, and then evaluate its performance. Here's a step-by-step guide on how to do this: Step 1: Gather Data We'll need historical data on soccer matches. This can include: Match results (wins, losses, draws) Team statistics (goals scored, goals conceded, possession, shots on target, etc.) Player statistics (goals, assists, injuries, etc.) Venue information (home/away) Team form (recent performance) Step 2: Preprocess Data Clean the data and create features that will help the model learn patterns. This might involve: Handling missing values Normalizing/standardizing numerical features Encoding categorical variables (e.g., team names, venues) Creating aggregate statistics (e.g., average goals per match) Step 3: Choose a Model For predicting match outcomes, we can use classification models such as: Logistic Regression Dec
predict future soccer match
Training a data model to predict future soccer match outcomes involves several steps. We'll need to gather relevant data, preprocess it, choose an appropriate model, train it, and then evaluate its performance. Here's a step-by-step guide on how to do this: Step 1: Gather Data We'll need historical data on soccer matches. This can include: Match results (wins, losses, draws) Team statistics (goals scored, goals conceded, possession, shots on target, etc.) Player statistics (goals, assists, injuries, etc.) Venue information (home/away) Team form (recent performance) Step 2: Preprocess Data Clean the data and create features that will help the model learn patterns. This might involve: Handling missing values Normalizing/standardizing numerical features Encoding categorical variables (e.g., team names, venues) Creating aggregate statistics (e.g., average goals per match) Step 3: Choose a Model For predicting match outcomes, we can use classification models such as: Logistic Regression Dec
predict future soccer match
11948071
Autisms Detection
11948079
Training a data model to predict future soccer match outcomes involves several steps. We'll need to gather relevant data, preprocess it, choose an appropriate model, train it, and then evaluate its performance. Here's a step-by-step guide on how to do this: Step 1: Gather Data We'll need historical data on soccer matches. This can include: Match results (wins, losses, draws) Team statistics (goals scored, goals conceded, possession, shots on target, etc.) Player statistics (goals, assists, injuries, etc.) Venue information (home/away) Team form (recent performance) Step 2: Preprocess Data Clean the data and create features that will help the model learn patterns. This might involve: Handling missing values Normalizing/standardizing numerical features Encoding categorical variables (e.g., team names, venues) Creating aggregate statistics (e.g., average goals per match) Step 3: Choose a Model For predicting match outcomes, we can use classification models such as: Logistic Regression Dec
predict future soccer match
Training a data model to predict future soccer match outcomes involves several steps. We'll need to gather relevant data, preprocess it, choose an appropriate model, train it, and then evaluate its performance. Here's a step-by-step guide on how to do this: Step 1: Gather Data We'll need historical data on soccer matches. This can include: Match results (wins, losses, draws) Team statistics (goals scored, goals conceded, possession, shots on target, etc.) Player statistics (goals, assists, injuries, etc.) Venue information (home/away) Team form (recent performance) Step 2: Preprocess Data Clean the data and create features that will help the model learn patterns. This might involve: Handling missing values Normalizing/standardizing numerical features Encoding categorical variables (e.g., team names, venues) Creating aggregate statistics (e.g., average goals per match) Step 3: Choose a Model For predicting match outcomes, we can use classification models such as: Logistic Regression Dec
predict future soccer match
Training a data model to predict future soccer match outcomes involves several steps. We'll need to gather relevant data, preprocess it, choose an appropriate model, train it, and then evaluate its performance. Here's a step-by-step guide on how to do this: Step 1: Gather Data We'll need historical data on soccer matches. This can include: Match results (wins, losses, draws) Team statistics (goals scored, goals conceded, possession, shots on target, etc.) Player statistics (goals, assists, injuries, etc.) Venue information (home/away) Team form (recent performance) Step 2: Preprocess Data Clean the data and create features that will help the model learn patterns. This might involve: Handling missing values Normalizing/standardizing numerical features Encoding categorical variables (e.g., team names, venues) Creating aggregate statistics (e.g., average goals per match) Step 3: Choose a Model For predicting match outcomes, we can use classification models such as: Logistic Regression Dec
predict future soccer match
Training a data model to predict future soccer match outcomes involves several steps. We'll need to gather relevant data, preprocess it, choose an appropriate model, train it, and then evaluate its performance. Here's a step-by-step guide on how to do this: Step 1: Gather Data We'll need historical data on soccer matches. This can include: Match results (wins, losses, draws) Team statistics (goals scored, goals conceded, possession, shots on target, etc.) Player statistics (goals, assists, injuries, etc.) Venue information (home/away) Team form (recent performance) Step 2: Preprocess Data Clean the data and create features that will help the model learn patterns. This might involve: Handling missing values Normalizing/standardizing numerical features Encoding categorical variables (e.g., team names, venues) Creating aggregate statistics (e.g., average goals per match) Step 3: Choose a Model For predicting match outcomes, we can use classification models such as: Logistic Regression Dec
predict future soccer match
11948071
Autisms Detection
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Experience
Computer Engineer
Sep, 2023 - Present
•
1 year, 3 months
I-Tech
Sep, 2023 - Present
•
1 year, 3 months
Working on different project in web development and machine learning.
Sep, 2023 - Present
•
1 year, 3 months
Education
Institute of Space Technology
2019 - 2023
•
4 years
Computer Engineering
Flag of
Pakistan
2019 - 2023
•
4 years
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