#EXCEL DATA ANALYSIS REGRESSION DOWNLOAD HOW TO#

The following is a sample Python code snippet demonstrating how to train a linear regression model using the Boston Housing Dataset. Python Code for Training Linear Regression Models using CSV Dataset Description: Predicting the burned area of forest fires based on meteorological data.

Description: Predicting graduate admission chance based on various factors.

Description: Predicting bike rental count based on time and weather factors.

Description: Predicting student performance based on demographic and school-related factors.Description: Predicting abalone age based on physical measurements.Description: Predicting global video game sales based on platform, genre, etc.Description: Predicting energy consumption of appliances and lights based on various factors.Description: Predicting concrete compressive strength based on its ingredients.Target: Typically species, but can be adapted to a continuous variable for regression.Features: Sepal length, sepal width, petal length, petal width.Description: Though typically used for classification, it can be adapted for regression.Target: A quantitative measure of disease progression one year after baseline.Contains ten baseline variables, age, sex, body mass index, average blood pressure, and six blood serum measurements. Description: Predicting disease progression based on diagnostic measurements.Description: Predicting house prices based on real estate features.Description: Predicting sales based on advertising channels.Target: Quality of wine on a scale of 0–10.Includes features like alcohol, sugar, pH level, etc. Contains physicochemical (inputs) and sensory (the output) variables based on wine samples. Description: Predicting wine quality based on physicochemical properties.Description: Predicting medical costs based on patient information.Description: Predicting fuel efficiency (MPG) of cars based on 7 features.Source: Available in the sklearn.datasets module.Target: Median house value for California districts.Consists of 8 features including median income, housing median age, average rooms, etc. Description: This dataset contains housing data from the 1990 California census.Target: Median value of owner-occupied homes.Predicting house prices based on 13 features such as CRIM (crime rate), RM (average number of rooms), AGE (proportion of owner-occupied units built prior to 1940), etc. Description: Contains information about the housing values in the suburbs of Boston.The following is a list of 15 dataset which you can use to train linear regression models for learning purpose: List of Dataset for Training Linear Regression Models Python Code for Training Linear Regression Models using CSV Dataset.List of Dataset for Training Linear Regression Models.

Excel data analysis regression download