EARTHQUAKE-PREDICTION-MODEL-USING-PYTHON

In this section we going to provide the dependencies/package installation and contents of our project.

1.INTRODUCTION

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.

2.PREREQUISITES

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

1.Numpy:

 pip install numpy

If you use pip, you can install NumPy

2.Pandas

pip install pandas

Pandas can be installed via pip from PyPI.

3.Seaborn

Official releases of seaborn can be installed from PyPI: "pip install seaborn"

4.Sci-kit learn

pip install scikit-learn

Sklearn can be installed via pip from PyPI

5.Matplotlib

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:

6.Linear regression model

"from sklearn.linear_model import LinearRegression"

Here we going to import thr model only by using the sci-kit learn library

7.SVM

"from sklearn.svm import SVR"

Here we going to import thr model only by using the sci-kit learn library.

8.Random Forest

"from sklearn.ensemble import RandomForestRegressor"