In order to use this model you need to create the environment on you computer.This is a Keras easy type data for beginners of Data Science. Data consists of 13 different features in both train and test input datas. Our output data is average prices of the 404 accomadations in Boston,USA.we needed to predict prices of 102 houses in test data.I used neural networks to increase the accuracy rate rather than using LinearRegression model from scikit-learn library.
You can compare the prices of that area
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First click the buttons windows+R and type cmd in box below clone my model from github on the black window
C:\> git clone https://github.com/Mukhriddin19980901/boston_housing.git -
Write this command on black window.
C:\> cd boston_housing
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You need to upgrade your pip command to create environment
C:\boston_housing>python.exe -m pip install --upgrade pip -
Here you need to install environment module and you can create your virtual environment
C:\boston_housing>python -m venv pip install --user virtualenv -
Give the name to the environmentyou can give any name instead environment_name)
C:\boston_housing>python -m venv environment_name -
Then you need to activate the environment
C:\boston_housing>environment_name\Scripts\activate.bat -
Install all required libraries from the requirements.txt file
(environment_name) C:\boston_housing> pip install -r requirements.txt -
Now you can work on jupyter notebook
(environment_name) C:\boston_housing>jupyter notebook
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Now you can see the code here.I used mean squared error loss function and rmsprop (Root Mean Squared Propagation,)
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The model summary :
- Here you can compare the flactuation of accuracy of predictions and real prices after every epoch.
🔴 If you find it useful give a star to this repo and follow me on Kaggle and Linkedin