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$13 USD / hour
Flag of PAKISTAN
nowshera cantt, pakistan
$13 USD / hour
It's currently 4:51 PM here
Joined August 29, 2020
0 Recommendations

Muhammad Amir K.

@AmirKhan135

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$13 USD / hour
Flag of PAKISTAN
nowshera cantt, pakistan
$13 USD / hour
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Data Science | ML | Python | AWS

Provide me with your data and let it tell stories. With over 3 years of industry experience, I am an IBM Certified Data Science Professional and Machine Learning expert. I have done over 50 data science and machine learning projects and certifications. I am highly skilled in python for data science and machine learning I can run end-to-end data science pipeline from data collection using BeautifulSoup, Scrapy, and Selenium and other techniques to data cleaning and analysis using Numpy, Pandas to data visualization using Matplotlib, Seaborn, and Plotly followed by Machine Learning [Supervised, Unsupervised, Clustering] using sklearn, Tensorflow, and Keras. I have sound knowledge and experience of AWS services like AWS Lambda, AWS DynanoDB, AWS S3, AWS Kinesis, and other AWS services My Top Skills are: Data Science, Machine Learning, Data Analysis, Data Visualisation, Web Scraping, Data Collection, Python, Numpy, Pandas, Supervised Learning, Unsupervised Learning, Clustering, Matplotlib, Seaborn, Plotly, sklearn, TensorFlow, Keras, Scrapy, BeautifuSoup AWS Services
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Portfolio

7116650
Using real data of a real estate company from Oslo, I created a house prediction model using Python. I imported data from CSVs into data frames, did data analysis. I checked for duplicates rows, missing data, and outliers in the data. I then removed some of the highly correlates features. Finally, I made the data ready for machine learning models and applied different regression algorithms like SVM, kNR, LightGBM. When I was satisfied with the results of ML algorithms, I saved the best performing for future predictions. Libraries I used: Numpy, Pandas, Matplotlib
House Price Prediction Using Real Data
Using real data of a real estate company from Oslo, I created a house prediction model using Python. I imported data from CSVs into data frames, did data analysis. I checked for duplicates rows, missing data, and outliers in the data. I then removed some of the highly correlates features. Finally, I made the data ready for machine learning models and applied different regression algorithms like SVM, kNR, LightGBM. When I was satisfied with the results of ML algorithms, I saved the best performing for future predictions. Libraries I used: Numpy, Pandas, Matplotlib
House Price Prediction Using Real Data
Using real data of a real estate company from Oslo, I created a house prediction model using Python. I imported data from CSVs into data frames, did data analysis. I checked for duplicates rows, missing data, and outliers in the data. I then removed some of the highly correlates features. Finally, I made the data ready for machine learning models and applied different regression algorithms like SVM, kNR, LightGBM. When I was satisfied with the results of ML algorithms, I saved the best performing for future predictions. Libraries I used: Numpy, Pandas, Matplotlib
House Price Prediction Using Real Data
Using real data of a real estate company from Oslo, I created a house prediction model using Python. I imported data from CSVs into data frames, did data analysis. I checked for duplicates rows, missing data, and outliers in the data. I then removed some of the highly correlates features. Finally, I made the data ready for machine learning models and applied different regression algorithms like SVM, kNR, LightGBM. When I was satisfied with the results of ML algorithms, I saved the best performing for future predictions. Libraries I used: Numpy, Pandas, Matplotlib
House Price Prediction Using Real Data
Using real data of a real estate company from Oslo, I created a house prediction model using Python. I imported data from CSVs into data frames, did data analysis. I checked for duplicates rows, missing data, and outliers in the data. I then removed some of the highly correlates features. Finally, I made the data ready for machine learning models and applied different regression algorithms like SVM, kNR, LightGBM. When I was satisfied with the results of ML algorithms, I saved the best performing for future predictions. Libraries I used: Numpy, Pandas, Matplotlib
House Price Prediction Using Real Data
Using real data of a real estate company from Oslo, I created a house prediction model using Python. I imported data from CSVs into data frames, did data analysis. I checked for duplicates rows, missing data, and outliers in the data. I then removed some of the highly correlates features. Finally, I made the data ready for machine learning models and applied different regression algorithms like SVM, kNR, LightGBM. When I was satisfied with the results of ML algorithms, I saved the best performing for future predictions. Libraries I used: Numpy, Pandas, Matplotlib
House Price Prediction Using Real Data
Using real data of a real estate company from Oslo, I created a house prediction model using Python. I imported data from CSVs into data frames, did data analysis. I checked for duplicates rows, missing data, and outliers in the data. I then removed some of the highly correlates features. Finally, I made the data ready for machine learning models and applied different regression algorithms like SVM, kNR, LightGBM. When I was satisfied with the results of ML algorithms, I saved the best performing for future predictions. Libraries I used: Numpy, Pandas, Matplotlib
House Price Prediction Using Real Data
7116549
In this project, I used Python to scrape a news website to extract Corona case data for Pakistan using. I scraped complete HTML of the page and then extracted elements of the page that contained corona information. I analyzed that information, imported it into a DataFrame, and then wrote the final results to different excel sheets. I also made interactive corona cases charts using Plotly library Libraries used: Numpy, Pandas, Plotly, read_excel
Web Scraping and Saving Results to Excel Sheets
In this project, I used Python to scrape a news website to extract Corona case data for Pakistan using. I scraped complete HTML of the page and then extracted elements of the page that contained corona information. I analyzed that information, imported it into a DataFrame, and then wrote the final results to different excel sheets. I also made interactive corona cases charts using Plotly library Libraries used: Numpy, Pandas, Plotly, read_excel
Web Scraping and Saving Results to Excel Sheets
In this project, I used Python to scrape a news website to extract Corona case data for Pakistan using. I scraped complete HTML of the page and then extracted elements of the page that contained corona information. I analyzed that information, imported it into a DataFrame, and then wrote the final results to different excel sheets. I also made interactive corona cases charts using Plotly library Libraries used: Numpy, Pandas, Plotly, read_excel
Web Scraping and Saving Results to Excel Sheets
In this project, I used Python to scrape a news website to extract Corona case data for Pakistan using. I scraped complete HTML of the page and then extracted elements of the page that contained corona information. I analyzed that information, imported it into a DataFrame, and then wrote the final results to different excel sheets. I also made interactive corona cases charts using Plotly library Libraries used: Numpy, Pandas, Plotly, read_excel
Web Scraping and Saving Results to Excel Sheets
In this project, I used Python to scrape a news website to extract Corona case data for Pakistan using. I scraped complete HTML of the page and then extracted elements of the page that contained corona information. I analyzed that information, imported it into a DataFrame, and then wrote the final results to different excel sheets. I also made interactive corona cases charts using Plotly library Libraries used: Numpy, Pandas, Plotly, read_excel
Web Scraping and Saving Results to Excel Sheets
7113817
A project with introduction to H2O AutoML using Python. The objective is to predict whether or not a customer will enroll in a bank's term loan This was a project on Automatic Machine Learning with H2O AutoML and Python. By doing this project, I am now able to describe what AutoML is and apply automatic machine learning to a business analytics problem with the H2O AutoML interface in Python. H2O's AutoML automates the process of training and tuning a large selection of models, allowing the user to focus on other aspects of the data science and machine learning pipelines such as data pre-processing, feature engineering, and model deployment. I took a dataset of a bank to predict whether or not a customer will buy a bank product. I created a H2O cluster, converted the pandas data frame into h2oframe, and then split the data into train and test sets followed by training via h2o cluster by setting the training time to 10 minutes rather than one hour.... [reach out for more details]
H2O AutoML Customer Loan Enrollment Prediction
A project with introduction to H2O AutoML using Python. The objective is to predict whether or not a customer will enroll in a bank's term loan This was a project on Automatic Machine Learning with H2O AutoML and Python. By doing this project, I am now able to describe what AutoML is and apply automatic machine learning to a business analytics problem with the H2O AutoML interface in Python. H2O's AutoML automates the process of training and tuning a large selection of models, allowing the user to focus on other aspects of the data science and machine learning pipelines such as data pre-processing, feature engineering, and model deployment. I took a dataset of a bank to predict whether or not a customer will buy a bank product. I created a H2O cluster, converted the pandas data frame into h2oframe, and then split the data into train and test sets followed by training via h2o cluster by setting the training time to 10 minutes rather than one hour.... [reach out for more details]
H2O AutoML Customer Loan Enrollment Prediction
A project with introduction to H2O AutoML using Python. The objective is to predict whether or not a customer will enroll in a bank's term loan This was a project on Automatic Machine Learning with H2O AutoML and Python. By doing this project, I am now able to describe what AutoML is and apply automatic machine learning to a business analytics problem with the H2O AutoML interface in Python. H2O's AutoML automates the process of training and tuning a large selection of models, allowing the user to focus on other aspects of the data science and machine learning pipelines such as data pre-processing, feature engineering, and model deployment. I took a dataset of a bank to predict whether or not a customer will buy a bank product. I created a H2O cluster, converted the pandas data frame into h2oframe, and then split the data into train and test sets followed by training via h2o cluster by setting the training time to 10 minutes rather than one hour.... [reach out for more details]
H2O AutoML Customer Loan Enrollment Prediction
A project with introduction to H2O AutoML using Python. The objective is to predict whether or not a customer will enroll in a bank's term loan This was a project on Automatic Machine Learning with H2O AutoML and Python. By doing this project, I am now able to describe what AutoML is and apply automatic machine learning to a business analytics problem with the H2O AutoML interface in Python. H2O's AutoML automates the process of training and tuning a large selection of models, allowing the user to focus on other aspects of the data science and machine learning pipelines such as data pre-processing, feature engineering, and model deployment. I took a dataset of a bank to predict whether or not a customer will buy a bank product. I created a H2O cluster, converted the pandas data frame into h2oframe, and then split the data into train and test sets followed by training via h2o cluster by setting the training time to 10 minutes rather than one hour.... [reach out for more details]
H2O AutoML Customer Loan Enrollment Prediction
7113765
Link to the project included This data analysis and data visualization project uses entirely Python language. It enables users to set parameters (columns from the dataset) and visualize and analyze data accordingly. I have used Numpy, Pandas, Plotly and Streamlit libraries in this project https://github.com/muhammadamirkhan/Python-Data-Science-Web-App-Using-Streamlit-
Data Analysis and Real Time Data Visualization Using Python
7113710
Link to the project included This project introduces a machine learning classification web app using Python. It enables users to select from 3 machine learning algorithms SVM, Logistic Regression, and RandomForest. Then the user sets hyperparameters according to the algorithm selected. Finally, the user has simply had to hit the Classify button to run the classification. It classifies mushrooms to either of the classes [Edible, Poisnous] https://github.com/muhammadamirkhan/Data-Science-and-ML-Projects/tree/master/Machine%20Learning%20Web%20App%20Using%20Streamlit
Machine Learning Classification Web App
Link to the project included This project introduces a machine learning classification web app using Python. It enables users to select from 3 machine learning algorithms SVM, Logistic Regression, and RandomForest. Then the user sets hyperparameters according to the algorithm selected. Finally, the user has simply had to hit the Classify button to run the classification. It classifies mushrooms to either of the classes [Edible, Poisnous] https://github.com/muhammadamirkhan/Data-Science-and-ML-Projects/tree/master/Machine%20Learning%20Web%20App%20Using%20Streamlit
Machine Learning Classification Web App
Link to the project included This project introduces a machine learning classification web app using Python. It enables users to select from 3 machine learning algorithms SVM, Logistic Regression, and RandomForest. Then the user sets hyperparameters according to the algorithm selected. Finally, the user has simply had to hit the Classify button to run the classification. It classifies mushrooms to either of the classes [Edible, Poisnous] https://github.com/muhammadamirkhan/Data-Science-and-ML-Projects/tree/master/Machine%20Learning%20Web%20App%20Using%20Streamlit
Machine Learning Classification Web App
7116650
Using real data of a real estate company from Oslo, I created a house prediction model using Python. I imported data from CSVs into data frames, did data analysis. I checked for duplicates rows, missing data, and outliers in the data. I then removed some of the highly correlates features. Finally, I made the data ready for machine learning models and applied different regression algorithms like SVM, kNR, LightGBM. When I was satisfied with the results of ML algorithms, I saved the best performing for future predictions. Libraries I used: Numpy, Pandas, Matplotlib
House Price Prediction Using Real Data
Using real data of a real estate company from Oslo, I created a house prediction model using Python. I imported data from CSVs into data frames, did data analysis. I checked for duplicates rows, missing data, and outliers in the data. I then removed some of the highly correlates features. Finally, I made the data ready for machine learning models and applied different regression algorithms like SVM, kNR, LightGBM. When I was satisfied with the results of ML algorithms, I saved the best performing for future predictions. Libraries I used: Numpy, Pandas, Matplotlib
House Price Prediction Using Real Data
Using real data of a real estate company from Oslo, I created a house prediction model using Python. I imported data from CSVs into data frames, did data analysis. I checked for duplicates rows, missing data, and outliers in the data. I then removed some of the highly correlates features. Finally, I made the data ready for machine learning models and applied different regression algorithms like SVM, kNR, LightGBM. When I was satisfied with the results of ML algorithms, I saved the best performing for future predictions. Libraries I used: Numpy, Pandas, Matplotlib
House Price Prediction Using Real Data
Using real data of a real estate company from Oslo, I created a house prediction model using Python. I imported data from CSVs into data frames, did data analysis. I checked for duplicates rows, missing data, and outliers in the data. I then removed some of the highly correlates features. Finally, I made the data ready for machine learning models and applied different regression algorithms like SVM, kNR, LightGBM. When I was satisfied with the results of ML algorithms, I saved the best performing for future predictions. Libraries I used: Numpy, Pandas, Matplotlib
House Price Prediction Using Real Data
Using real data of a real estate company from Oslo, I created a house prediction model using Python. I imported data from CSVs into data frames, did data analysis. I checked for duplicates rows, missing data, and outliers in the data. I then removed some of the highly correlates features. Finally, I made the data ready for machine learning models and applied different regression algorithms like SVM, kNR, LightGBM. When I was satisfied with the results of ML algorithms, I saved the best performing for future predictions. Libraries I used: Numpy, Pandas, Matplotlib
House Price Prediction Using Real Data
Using real data of a real estate company from Oslo, I created a house prediction model using Python. I imported data from CSVs into data frames, did data analysis. I checked for duplicates rows, missing data, and outliers in the data. I then removed some of the highly correlates features. Finally, I made the data ready for machine learning models and applied different regression algorithms like SVM, kNR, LightGBM. When I was satisfied with the results of ML algorithms, I saved the best performing for future predictions. Libraries I used: Numpy, Pandas, Matplotlib
House Price Prediction Using Real Data
Using real data of a real estate company from Oslo, I created a house prediction model using Python. I imported data from CSVs into data frames, did data analysis. I checked for duplicates rows, missing data, and outliers in the data. I then removed some of the highly correlates features. Finally, I made the data ready for machine learning models and applied different regression algorithms like SVM, kNR, LightGBM. When I was satisfied with the results of ML algorithms, I saved the best performing for future predictions. Libraries I used: Numpy, Pandas, Matplotlib
House Price Prediction Using Real Data
7116549
In this project, I used Python to scrape a news website to extract Corona case data for Pakistan using. I scraped complete HTML of the page and then extracted elements of the page that contained corona information. I analyzed that information, imported it into a DataFrame, and then wrote the final results to different excel sheets. I also made interactive corona cases charts using Plotly library Libraries used: Numpy, Pandas, Plotly, read_excel
Web Scraping and Saving Results to Excel Sheets
In this project, I used Python to scrape a news website to extract Corona case data for Pakistan using. I scraped complete HTML of the page and then extracted elements of the page that contained corona information. I analyzed that information, imported it into a DataFrame, and then wrote the final results to different excel sheets. I also made interactive corona cases charts using Plotly library Libraries used: Numpy, Pandas, Plotly, read_excel
Web Scraping and Saving Results to Excel Sheets
In this project, I used Python to scrape a news website to extract Corona case data for Pakistan using. I scraped complete HTML of the page and then extracted elements of the page that contained corona information. I analyzed that information, imported it into a DataFrame, and then wrote the final results to different excel sheets. I also made interactive corona cases charts using Plotly library Libraries used: Numpy, Pandas, Plotly, read_excel
Web Scraping and Saving Results to Excel Sheets
In this project, I used Python to scrape a news website to extract Corona case data for Pakistan using. I scraped complete HTML of the page and then extracted elements of the page that contained corona information. I analyzed that information, imported it into a DataFrame, and then wrote the final results to different excel sheets. I also made interactive corona cases charts using Plotly library Libraries used: Numpy, Pandas, Plotly, read_excel
Web Scraping and Saving Results to Excel Sheets
In this project, I used Python to scrape a news website to extract Corona case data for Pakistan using. I scraped complete HTML of the page and then extracted elements of the page that contained corona information. I analyzed that information, imported it into a DataFrame, and then wrote the final results to different excel sheets. I also made interactive corona cases charts using Plotly library Libraries used: Numpy, Pandas, Plotly, read_excel
Web Scraping and Saving Results to Excel Sheets
7113817
A project with introduction to H2O AutoML using Python. The objective is to predict whether or not a customer will enroll in a bank's term loan This was a project on Automatic Machine Learning with H2O AutoML and Python. By doing this project, I am now able to describe what AutoML is and apply automatic machine learning to a business analytics problem with the H2O AutoML interface in Python. H2O's AutoML automates the process of training and tuning a large selection of models, allowing the user to focus on other aspects of the data science and machine learning pipelines such as data pre-processing, feature engineering, and model deployment. I took a dataset of a bank to predict whether or not a customer will buy a bank product. I created a H2O cluster, converted the pandas data frame into h2oframe, and then split the data into train and test sets followed by training via h2o cluster by setting the training time to 10 minutes rather than one hour.... [reach out for more details]
H2O AutoML Customer Loan Enrollment Prediction
A project with introduction to H2O AutoML using Python. The objective is to predict whether or not a customer will enroll in a bank's term loan This was a project on Automatic Machine Learning with H2O AutoML and Python. By doing this project, I am now able to describe what AutoML is and apply automatic machine learning to a business analytics problem with the H2O AutoML interface in Python. H2O's AutoML automates the process of training and tuning a large selection of models, allowing the user to focus on other aspects of the data science and machine learning pipelines such as data pre-processing, feature engineering, and model deployment. I took a dataset of a bank to predict whether or not a customer will buy a bank product. I created a H2O cluster, converted the pandas data frame into h2oframe, and then split the data into train and test sets followed by training via h2o cluster by setting the training time to 10 minutes rather than one hour.... [reach out for more details]
H2O AutoML Customer Loan Enrollment Prediction
A project with introduction to H2O AutoML using Python. The objective is to predict whether or not a customer will enroll in a bank's term loan This was a project on Automatic Machine Learning with H2O AutoML and Python. By doing this project, I am now able to describe what AutoML is and apply automatic machine learning to a business analytics problem with the H2O AutoML interface in Python. H2O's AutoML automates the process of training and tuning a large selection of models, allowing the user to focus on other aspects of the data science and machine learning pipelines such as data pre-processing, feature engineering, and model deployment. I took a dataset of a bank to predict whether or not a customer will buy a bank product. I created a H2O cluster, converted the pandas data frame into h2oframe, and then split the data into train and test sets followed by training via h2o cluster by setting the training time to 10 minutes rather than one hour.... [reach out for more details]
H2O AutoML Customer Loan Enrollment Prediction
A project with introduction to H2O AutoML using Python. The objective is to predict whether or not a customer will enroll in a bank's term loan This was a project on Automatic Machine Learning with H2O AutoML and Python. By doing this project, I am now able to describe what AutoML is and apply automatic machine learning to a business analytics problem with the H2O AutoML interface in Python. H2O's AutoML automates the process of training and tuning a large selection of models, allowing the user to focus on other aspects of the data science and machine learning pipelines such as data pre-processing, feature engineering, and model deployment. I took a dataset of a bank to predict whether or not a customer will buy a bank product. I created a H2O cluster, converted the pandas data frame into h2oframe, and then split the data into train and test sets followed by training via h2o cluster by setting the training time to 10 minutes rather than one hour.... [reach out for more details]
H2O AutoML Customer Loan Enrollment Prediction
7113765
Link to the project included This data analysis and data visualization project uses entirely Python language. It enables users to set parameters (columns from the dataset) and visualize and analyze data accordingly. I have used Numpy, Pandas, Plotly and Streamlit libraries in this project https://github.com/muhammadamirkhan/Python-Data-Science-Web-App-Using-Streamlit-
Data Analysis and Real Time Data Visualization Using Python
7113710
Link to the project included This project introduces a machine learning classification web app using Python. It enables users to select from 3 machine learning algorithms SVM, Logistic Regression, and RandomForest. Then the user sets hyperparameters according to the algorithm selected. Finally, the user has simply had to hit the Classify button to run the classification. It classifies mushrooms to either of the classes [Edible, Poisnous] https://github.com/muhammadamirkhan/Data-Science-and-ML-Projects/tree/master/Machine%20Learning%20Web%20App%20Using%20Streamlit
Machine Learning Classification Web App
Link to the project included This project introduces a machine learning classification web app using Python. It enables users to select from 3 machine learning algorithms SVM, Logistic Regression, and RandomForest. Then the user sets hyperparameters according to the algorithm selected. Finally, the user has simply had to hit the Classify button to run the classification. It classifies mushrooms to either of the classes [Edible, Poisnous] https://github.com/muhammadamirkhan/Data-Science-and-ML-Projects/tree/master/Machine%20Learning%20Web%20App%20Using%20Streamlit
Machine Learning Classification Web App
Link to the project included This project introduces a machine learning classification web app using Python. It enables users to select from 3 machine learning algorithms SVM, Logistic Regression, and RandomForest. Then the user sets hyperparameters according to the algorithm selected. Finally, the user has simply had to hit the Classify button to run the classification. It classifies mushrooms to either of the classes [Edible, Poisnous] https://github.com/muhammadamirkhan/Data-Science-and-ML-Projects/tree/master/Machine%20Learning%20Web%20App%20Using%20Streamlit
Machine Learning Classification Web App

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Experience

Data Analyst

VentureDive
Feb 2020 - Present
I am an IBM certified data science and machine learning professional working as a data analyst. I have done 50+ data science projects and certifications from IBM, DataCamp, Udacity, and other websites. My day to day work includes data extraction, data collection, data merging, data cleaning, data transformations, data preprocessing, data visualization, machine learning, and presenting the findings to stakeholders. I can help with all of the above-mentioned tasks and any other data related task.

Software Engineer (Big Data & AWS)

Northbay Solutions
Aug 2019 - Feb 2020 (6 months, 1 day)
At Northbay Solutions, I got the chance to work on different AWS services like: AWS Lambda, AWS S3, AWS Kinesis, AWS DynamoDB I also explored PySpark Basics and worked on ReactJS and NodeJS

Data Science Intern

Dice Analytics
Jul 2018 - Jul 2019 (1 year)
As a data science intern, my responsibilities included, web scraping, data collection from different sources, combining them into a single source, data quality assessment, data cleaning, visualizing data and them feeding the data to a machine learning model to train and make predictions. I worked on different tools and languages like Python, Jupyter Notebook, PYCharm. I used many analysis techniques and ML algorithms like KNN, SVM, Naive Bayes, XGBoost, CatBoost, RandomForest, and others.

Education

BE Computer Engineering

National University of Science and Technology, Pakistan 2015 - 2019
(4 years)

Qualifications

Python for Data Science

Coursera
2019
See verified credentials at https://www.coursera.org/account/accomplishments/verify/3J388NYKKFWZ In this course on python for data science, I learned and explored different python's data analysis libraries like Numpy, Pandas. I learned various data analysis techniques using python like slicing data, merging data, grouping data and many other

Data Visualization with Python

IBM (Coursera)
2019
See verified credentials at https://www.coursera.org/account/accomplishments/certificate/2T97DEPRBDF5 In this course, I was introduced to several data visualization libraries in Python, namely Matplotlib, Seaborn, and Folium to create different type of charts like bar charts, histograms, line charts, scatter plots and more

Intermediate Machine Learning.

Kaggle
2020
See verified credentials at https://www.kaggle.com/learn/certification/ak1352/intermediate-machine-learning In this practical course on machine learning, I learned about missing values, categorical variables, ML pipelines, cross-validation, XGBoost, and data leakage

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Top Skills

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Amazon Web Services

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