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Creating prediction classification models using KNN to predict if a passenger with survive the titanic or not and using Naive Bayes to predict if an email is a spam or not

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hypothyroid-classification

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Advert-success-analysis

A Kenyan entrepreneur has created an online cryptography course and would want to advertise it on her blog. She currently targets audiences originating from various countries. In the past, she ran ads to advertise a related course on the same blog and collected data in the process. She would now like to identify which individuals are most likely to click on her ads.

Market-basket-analysis

EDA and Market basket analysis of Supermarket data, to uncover the most purchased items in home care department and the items that are likely to be purchased together

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House-prediction-using-R

This is a project on prediction of house prices in Seattle. The aim is to find features that contribute most to house prices and build a model for the prediction of house prices.

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House-price-prediction

Creating Regression model using different techniques that will predict the price of house given the number of bedrooms,size of living area,size of basement,number of floors,year it was built,year it was renovated,the location, availability waterfront and view,the grading of the house the size above and the condition of the house. I will create the model using Multiple linear regression,Quantile regression,Lasso regression,ridge regression and Elastic net. Then I will choose the best model.

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Sendy-Logistic-Challenge

Our main objective is to build a model that predict the estimated time of delivery of orders, from the point of driver pickup to the point of arrival at final destination. The solution will help Sendy enhance customer communication and improve the reliability of its service; which will ultimately improve customer experience. In addition, the solution will enable Sendy to realise cost savings, and ultimately reduce the cost of doing business, through improved resource management and planning for order scheduling.

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Twitter-sentiment-analysis

Predicting what type of sentiments will be expressed depending on the type of tweet written and the location of the account. Find the best model to best predict the sentiments expressed over.Social media has become a huge part of our life. It connects people to the outer world. Social media provides a way to showcase our lives, discretely, conveniently, and on our own terms. People rely more on the posts and tweets shared on social networking sites like Twitter®, Facebook®, and Instagram®. It is anticipated that social media should guide people in getting correct and authentic information on Corona cases. There are various classification models used in machine learning. Depending on the features, accuracy, and MSE, a good model should be chosen, so it is easier to predict the sentiments that will be expressed before the tweet is written and posted

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waweru-wanjiru/Naive-Bayes-KNN-classification

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