Topic: "linearregression-gradientdescent"
coding-ai/machine_learning_cpp
Machine Learning C++
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Amshra267/ML-work
contains the content related to ML
Language: Python - Size: 22.9 MB - Last synced at: over 1 year ago - Pushed at: almost 4 years ago - Stars: 3 - Forks: 0
farhatizakaria/LinearRegression_GradientDescent
Python script for linear regression "Using Gradient Descent"
Language: Python - Size: 4.88 KB - Last synced at: about 2 years ago - Pushed at: over 5 years ago - Stars: 2 - Forks: 1
akashkriplani/bike-sharing-assigment
This project tackles BoomBikes' post-Covid revenue decline by predicting shared bike demand after the lockdown. A consulting company identifies key variables impacting demand in the American market. The goal is to model demand, aiding BoomBikes in adapting its strategy to meet customer expectations and navigate market dynamics.
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thehamzza/Food-Truck-Profit-Prediction-Using-Linear-Regression-Optimization
In this part of this exercise, you will implement linear regression with one variable to predict profits for a food truck. Suppose you are the CEO of a restaurant franchise and are considering different cities for opening a new outlet. The chain already has trucks in various cities, you have data for profits and populations from the cities. You would like to use this data to help you select which city to expand to next. The file ex1data1.txt contains the dataset for our linear regression problem. The first column is the population of a city and the second column is the profit of a food truck in that city. A negative value for profit indicates a loss.
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AnuraagRath/Linear-Regression-MachineLearning-from-Scratch
Using python we have created a Linear Regression Machine Learning Model from Scratch. We have implemented Gradient Descent to find the best 'm' (Slope) and 'b' (Intercept).
Language: Python - Size: 450 KB - Last synced at: over 1 year ago - Pushed at: over 4 years ago - Stars: 0 - Forks: 0
Himanshu-srihsk/Gradient_descent_optimization-on-Boston-dataset
performed gradient descent on Linear Regression and optimized the result of boston data using gradient descent data
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