machine learning

[Sea1men]

Assume that you are hired as a Machine Learning Engineer by the Housing Agency, and you are asked to analyze the relationship between interest rates of housing loans and housing prices to predict future housing market trends.
(i) Identify suitable data set features that can be used for the above case.
(ii) Generate synthetic/dummy data set with 50 records.
(iii) Identify a suitable regression model to model the above-mentioned scenario. (iv) Implement the regression model you identified using Python and Google Colaboratory environment


mekata answer danna kenek nadda bn..

 

[residue]

@හෙළයෙක්

 

[Sea1men]

@හෙළයෙක්

help me bro. plz

 

[poopoo]

subbed

 

[tharuka1989]

https://sist.sathyabama.ac.in/sist_naac/documents/1.3.4/1822-b.e-ece-batchno-120.pdf

 

[Honda.putha]

Assume that you are hired as a Machine Learning Engineer by the Housing Agency, and you are asked to analyze the relationship between interest rates of housing loans and housing prices to predict future housing market trends.
(i) Identify suitable data set features that can be used for the above case.
(ii) Generate synthetic/dummy data set with 50 records.
(iii) Identify a suitable regression model to model the above-mentioned scenario. (iv) Implement the regression model you identified using Python and Google Colaboratory environment


mekata answer danna kenek nadda bn..

Linear Regression.

https://www.kaggle.com/code/ashydv/housing-price-prediction-linear-regression

uttare.

 

[topkollek]

Sure! Let's break down your requirements step by step.

(i) Identify suitable data set features:​

For the analysis of the relationship between interest rates and housing prices, the most immediate features we'd need are:

1. Interest Rate: This would be the primary independent variable of interest. This can be the average monthly or yearly interest rate for housing loans.
2. Housing Price: This would be the primary dependent variable, and it can represent the average monthly or yearly housing price in a given area.

Besides these primary features, additional features that can influence housing prices include:

1. GDP: A country or region's gross domestic product can have an influence on housing prices.
2. Unemployment Rate: Areas with high unemployment might have lower housing demand and thus potentially lower prices.
3. Population Growth: A rapidly growing population can increase housing demand.
4. Supply of Houses: The number of houses available for sale in a market can influence prices.
5. Loan Amount: The average loan amount people are taking. This can be influenced by interest rates but can also provide an indication of people's purchasing power.

However, for simplicity, we will only consider "Interest Rate" as our independent variable and "Housing Price" as our dependent variable for generating the synthetic data and modeling.
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//ChatGPT

 

[Geshi95]

බම්ප්

 

[හෙළයෙක්]

https://machinelearningmastery.com/feature-selection-with-real-and-categorical-data/

Linear Regression.

https://www.kaggle.com/code/ashydv/housing-price-prediction-linear-regression

uttare.

Meke okama thiyenawa ne.
Additional oya uda dapu tutorial ekath balana

 

[garfieldzz]

UP

 

[Sea1men]

https://sist.sathyabama.ac.in/sist_naac/documents/1.3.4/1822-b.e-ece-batchno-120.pdf

Linear Regression.

https://www.kaggle.com/code/ashydv/housing-price-prediction-linear-regression

uttare.

https://machinelearningmastery.com/feature-selection-with-real-and-categorical-data/



Meke okama thiyenawa ne.
Additional oya uda dapu tutorial ekath balana

Thanx machnla hamotama