Predict The Weather with Machine Learning: Beginner Project

This project demonstrates how to predict local weather using machine learning techniques. It's designed as a beginner-friendly tutorial that walks you through downloading weather data, preparing it for machine learning, and using a Ridge Regression model to make predictions.

Project Overview

In this project, you will:

1. Download weather data
2. Clean and preprocess the data
3. Fill in missing values
4. Analyze weather data for insights
5. Train a Ridge Regression machine learning model
6. Evaluate the model's performance
7. Add additional features such as rolling means, monthly, and daily averages
8. Run diagnostics on the trained model

Key Features

• Languages: Python
• Tools: Jupyter Notebook, Pandas, Scikit-learn
• Machine Learning Model: Ridge Regression

Dataset

1. Access the NOAA website
   Go to NOAA Climate Data Online.

2. Search for Data

   • Enter the years you want (e.g., starting from 1970).
   • Search for the closest airport to you, or use your city/country name if no airport is available.

3. Add to Cart

   • Click Add to Cart for the selected airport/location.
   • If you can't find an airport nearby, try your city or country name instead.

4. Download Data

   • Go to your cart: NOAA Cart.
   • Select CSV format and click Continue.
   • Check all the boxes for data types.
   • Enter your email and click Continue.

5. Receive Download Link
   You'll receive an email with a link to download the data. Download the CSV file provided.

6. Data Documentation
   Review the NOAA data documentation to understand the column formats and data types.

Steps

1. Download the Data
   The first step is to download the dataset from the Dataquest repository.

2. Data Loading and Preprocessing
   We'll use Pandas to load the dataset into a DataFrame, clean it, and handle missing values.

3. Feature Engineering
   After analyzing the data, we create additional features such as rolling means, and monthly and daily averages to improve the model.

4. Model Training
   A Ridge Regression model is trained on the prepared dataset to predict the weather.

5. Model Evaluation
   We evaluate the model's performance using standard evaluation metrics and fine-tune it to improve accuracy.

6. Prediction Function
   A function is created to make predictions based on the model.