In this project, I have build a K-Nearest Neighbor classifier that is trained to predict whether a patient has benign or malignant breast cancer.
- My model would classify Benign & Malignant Breast Cancer with highest accuracy.
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In this project, I will be using a dataset containing census information from UCI’s Machine Learning Repository. By using this census data with a random forest, I will try to predict whether or not a person makes more than 50,000 Dollar.
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What are some of the features that would provide clue for defining Continent of a country from just their flag? Maybe some of the colors are good indicators. The presence or absence of certain shapes could provide a hint.
In this project, I've used decision trees to try to predict the continent of flags based on several of these features.The Flag Attribute Information for this dataset is from UCI’s Machine Learning Repository.
- From which Continent the Flag☝🏻 is from ?
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In this project, I've used building blocks of Neural Network: perceptrons to model the fundamental building blocks of computers — logic gates.
- AND gate - The table below shows the results of an AND gate. Given two inputs, an AND gate will output a 1 only if both inputs are a 1.
- XOR gate — a gate that outputs a 1 only if one of the inputs is a 1.
- AND gate can be thought of as linearly separable data and Perceptron can be trained to perform AND.
- XOR gate isn’t linearly separable and a Perceptron fails to learn XOR.
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Here I've deployed single, double and multiple features linear regression models, for feature selection and model tuning.
tennis_stats.csv is data from the men’s professional tennis league, which is called the ATP (Association of Tennis Professionals). Data from the top 1500 ranked players in the ATP over the span of 2009 to 2017 are provided in file. The statistics recorded for each player in each year include service game (offensive) statistics, return game (defensive) statistics and outcomes.
- To determine what it takes to be one of the best tennis players in the world.
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In this project I've build Regression model that predicts which passengers survived the sinking of the Titanic, based on features.The data I'll be using for training the model is provided by Kaggle Titanic competition!
Predicting what happened to:
- 3rd class passenger
Jack, - 1st class passenger
Roseand - 3rd class youngest passenger onboard
Millvina Dean
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The Honeybees are in a precarious state right now. There have been articles about the decline of the honeybee population for various reasons. This project is to investigate this decline and how the trends of the past predict the future for the honeybees.
DataFrame about honey production in the United States is collected from Kaggle.
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