In this section we going to provide the dependencies/package installation and contents of our project.
We are did a project which predicts the earthquake.here we have used linear regression,support vector machine and random forest which gives better accuracy of our model.it gives above 80% accuracy.
List the prerequisites and dependencies for running the project. Include instructions on how to install these dependencies. Dependencies needed:
1.Numpy 2.Pandas 3.Seaborn 4.Sci-kit learn 5.Matplotlib 6.Linear regression model 7.SVM 8.Random forest
Instructions to download these dependencies
pip install numpy
If you use pip, you can install NumPy
pip install pandas
Pandas can be installed via pip from PyPI.
Official releases of seaborn can be installed from PyPI: "pip install seaborn"
pip install scikit-learn
Sklearn can be installed via pip from PyPI
python -m pip install -U pip
python -m pip install -U matplotlib
Matplotlib releases are available as wheel packages for macOS, Windows and Linux on PyPI. Install it using pip:
"from sklearn.linear_model import LinearRegression"
Here we going to import thr model only by using the sci-kit learn library
"from sklearn.svm import SVR"
Here we going to import thr model only by using the sci-kit learn library.
"from sklearn.ensemble import RandomForestRegressor"
Here we going to import thr model only by using the sci-kit learn library.