Chatbot Project

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On progress


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Wine Quality Prediction(Logistic Regression) - Kaggle[Dataset]

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รูปภาพ
Overview from kaggle Context The two datasets are related to red and white variants of the Portuguese "Vinho Verde" wine. For more details, consult the reference [Cortez et al., 2009]. Due to privacy and logistic issues, only physicochemical (inputs) and sensory (the output) variables are available (e.g. there is no data about grape types, wine brand, wine selling price, etc.). These datasets can be viewed as classification or regression tasks. The classes are ordered and not balanced (e.g. there are much more normal wines than excellent or poor ones). This dataset is also available from the UCI machine learning repository,  https://archive.ics.uci.edu/ml/datasets/wine+quality  , I just shared it to kaggle for convenience. (If I am mistaken and the public license type disallowed me from doing so, I will take this down if requested.) Content For more information, read [Cortez et al., 2009]. Input variables (based on physicochemical tests): 1 - fixed acidity...
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Logistic Regression - Project

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รูปภาพ
Logistic Regression Project ¶ In this project I will be working with a fake advertising data set, indicating whether or not a particular internet user clicked on an Advertisement. I will try to create a model that will predict whether or not they will click on an ad based off the features of that user. This data set contains the following features: 'Daily Time Spent on Site': consumer time on site in minutes 'Age': cutomer age in years 'Area Income': Avg. Income of geographical area of consumer 'Daily Internet Usage': Avg. minutes a day consumer is on the internet 'Ad Topic Line': Headline of the advertisement 'City': City of consumer 'Male': Whether or not consumer was male 'Country': Country of consumer 'Timestamp': Time at which consumer clicked on Ad or closed window 'Clicked on Ad': 0 or 1 indicated clicking on Ad Import Libraries       First of all, import the lib...
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House Prices Prediction - Kaggle

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รูปภาพ
Competition Description 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. Practice Skills Creative feature engineering  Advanced regression techniques like random forest and gradient boosting Here, is result I has used gradient boosting regression for this project
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