[implemented by Scikit-learn]
Summary
Predicted daily temperature using multiple Linear Regression models & MLP with Scikit-learn, score = 0.85
Goal:
Using public local weather station data (San Jose) from NOAA (National Oceanic and
Atmospheric Administration) to predict temperature for the next 24 hours by using
different Machine Learning Algorithms
Raw Data:
NOAA weather data of San Jose weather station (2007.01.01 - 2016.12.31)
ML Algorithms (Scikit-learn):
linear_model.LinearRegression
linear_model.Lasso
linear_model.Ridge
neural_network.MLPRegressor
Plotting:
matplotlib
Envs:
Anaconda and Python 2.7
Packages:
conda create -n weather_prediction_py27 python=2.7
conda install numpy pandas matplotlib scikit-learn
conda install spyder
Run Steps:
% git clone https://github.com/jasonx1011/weather_prediction.git
% python weather_prediction.py
or
using spyder to run weather_prediction.py (Recommended)
Sample Outputs:
- Command Line Outputs:
- Plotting Outputs:
