Portfolio Project: Predicting The Weather Using Machine Learning
- Guided Project
- 1.4 hours
- Beginner
- Python
Practice using machine learning with Python and pandas to predict local weather patterns from historical data.
Overview
In this project, you'll assume the role of a data scientist tasked with predicting tomorrow's weather based on historical data from the Oakland International Airport. You'll apply skills in data preparation, time series analysis, and machine learning to develop a predictive model.
Throughout the project, you'll learn how to clean and structure weather data, create relevant features like rolling averages, and build a machine learning model to forecast temperatures. You'll evaluate your model's accuracy and learn techniques to fine-tune its performance.
Objective: Use historical weather data to develop a machine learning model that predicts tomorrow's temperature, building skills in data preparation, model creation, and evaluation.
What You'll Learn
- ✓ Load and clean weather data
- ✓ Work with time series data
- ✓ Implement a machine learning algorithm
Before You Start
- ✓ Working with Python data types, variables, and functions
- ✓ Implementing control flow statements like loops and conditionals
- ✓ Manipulating data using NumPy arrays and vectorized operations
- ✓ Exploring and preparing datasets using the pandas library
Project Steps
11 steps
- 1 Project Overview
- 2 Preparing the Data
- 3 Filling in Missing Data
- 4 Verifying Data Types
- 5 Analyzing Weather Data
- 6 Training an Initial Model
- 7 Measuring Accuracy
- 8 Building a Prediction Function
- 9 Adding in Rolling Means
- 10 Adding in Monthly and Daily Averages
- 11 Next Steps
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