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1. Models-development Models-development Public

   developing several models (Linear Regression, Multiple Linear Regression, and Polynomial Regression) that will predict the price of the car using the variables or features. Then evaluating these mo…

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2. Classification-methods Classification-methods Public

   load a dataset using Pandas and apply the following classification methods (KNN, Decision Tree, SVM, and Logistic Regression) to find the best one by accuracy evaluation methods (Jaccard, F1-score,…

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3. Predicting-Loan Predicting-Loan Public

   Loan prediction using Random Forest, Decision tree, SMOTE and SMOTETOMEK techniques.

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4. predicting-car-prices predicting-car-prices Public

   Predicting a car's market price using its attributes by the help of several Python's libraries including: pandas, numpy, skleran, and KNN classifier.

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5. Predicting-House-Prices Predicting-House-Prices Public

   Working with housing data for the city of Ames, Iowa, United States from 2006 to 2010 and then try to predict houses prices using pandas, numpy, sklearn and linear regression.

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6. Predicting-Bike-Rentals Predicting-Bike-Rentals Public

   Apply decision trees and random forests to predict the number of bike rentals.

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