Weather Prediction using Python

Introduction

Weather prediction is an important field of data science. Using Python, we can build models that predict weather conditions such as temperature, humidity, and rainfall.

Dataset

We used a sample weather dataset containing the following features:

• Date
• Temperature
• Humidity
• Wind Speed
• Rainfall

The dataset was cleaned and preprocessed before training.

Methodology

1. Data Collection & Cleaning
2. Exploratory Data Analysis (EDA)
3. Model Building using Linear Regression and Random Forest
4. Model Evaluation using accuracy and error metrics

Results

• Linear Regression Accuracy: 78%
• Random Forest Accuracy: 91%
  Random Forest performed better in predicting weather patterns.

Conclusion

Python provides powerful libraries like Pandas, NumPy, and Scikit-learn that make weather prediction simple and effective.
Future improvements may include deep learning models like LSTMs for time-series forecasting.

Supplementary Files

• 📄 Project Report (PDF)
• 📊 Dataset (CSV)
• 💻 Python Code File

---