mamady1999
I am passionate about data. My work as a data scientist consists in collecting, analyzing and communicating data to solve different problems.
IndependentGuinea, Conakry
Pinned Repositories
Biodiversity-in-National-Parks
Capstone-Project-Netflix-Data
Constellations
house-prices-advanced-regression-techniques
Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence. With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.
ICR---Identifying-Age-Related-Conditions
Use Machine Learning to detect conditions with measurements of anonymous characteristics
Life-Expectancy-and-GDP
machine-learning-workflow-udemy-course
OKCupid-Date-A-Scientist
In recent years, there has been a massive increase in the use of dating apps to find love. Many of these apps use sophisticated data science techniques to recommend possible matches to users and optimize the user experience. These apps give us access to a wealth of information we never had before about how how different people experience romance.
Outpatient-monitoring-and-management-of-insulin-dependent-diabetes-mellitus-IDDM-
Predict_Loan_Eligibility_for_Dream_Housing_Finance_company
Dream Housing Finance company deals in all kinds of home loans. They have presence across all urban, semi urban and rural areas. Customer first applies for home loan and after that company validates the customer eligibility for loan. Company wants to automate the loan eligibility process (real time) based on customer detail provided while filling online application form. These details are Gender, Marital Status, Education, Number of Dependents, Income, Loan Amount, Credit History and others. To automate this process, they have provided a dataset to identify the customers segments that are eligible for loan amount so that they can specifically target these customers.
mamady1999's Repositories
mamady1999/Outpatient-monitoring-and-management-of-insulin-dependent-diabetes-mellitus-IDDM-
mamady1999/ICR---Identifying-Age-Related-Conditions
Use Machine Learning to detect conditions with measurements of anonymous characteristics
mamady1999/Telecom-Churn-Prediction
In the telecom industry, customers are able to choose from multiple service providers and actively switch from one operator to another. In this highly competitive market, the telecommunications industry experiences an average of 15-25% annual churn rate. Given the fact that it costs 5-10 times more to acquire a new customer than to retain an existing one, customer retention has now become even more important than customer acquisition. For many incumbent operators, retaining high profitable customers is the number one business goal. To reduce customer churn, telecom companies need to predict which customers are at high risk of churn. In this project, you will analyze customer-level data of a leading telecom firm, build predictive models to identify customers at high risk of churn.
mamady1999/machine-learning-workflow-udemy-course
mamady1999/Predict_Loan_Eligibility_for_Dream_Housing_Finance_company
Dream Housing Finance company deals in all kinds of home loans. They have presence across all urban, semi urban and rural areas. Customer first applies for home loan and after that company validates the customer eligibility for loan. Company wants to automate the loan eligibility process (real time) based on customer detail provided while filling online application form. These details are Gender, Marital Status, Education, Number of Dependents, Income, Loan Amount, Credit History and others. To automate this process, they have provided a dataset to identify the customers segments that are eligible for loan amount so that they can specifically target these customers.
mamady1999/Price_forecast_of_the_home_contest_kaggle
Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence. With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.
mamady1999/house-prices-advanced-regression-techniques
Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence. With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.
mamady1999/OKCupid-Date-A-Scientist
In recent years, there has been a massive increase in the use of dating apps to find love. Many of these apps use sophisticated data science techniques to recommend possible matches to users and optimize the user experience. These apps give us access to a wealth of information we never had before about how how different people experience romance.
mamady1999/Biodiversity-in-National-Parks
mamady1999/Life-Expectancy-and-GDP
mamady1999/Capstone-Project-Netflix-Data
mamady1999/Constellations
mamady1999/U.S.-Medical-Insurance-Costs
The purpose of this project is to analyze a csv file called insurance.csv, in order to have clear information about the cost of insurance in the United States according to several criteria represented in our csv file.To complete the project, I will use the skills I have learned in previous classes.