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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…

    Jupyter Notebook 1

  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,…

    Jupyter Notebook 1

  3. Predicting-Loan Predicting-Loan Public

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

    Jupyter Notebook 1

  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.

    Jupyter Notebook

  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.

    Jupyter Notebook

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

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

    Jupyter Notebook