🌧️ Rainfall Prediction Project

This project uses the Austin Weather dataset to predict daily precipitation (rainfall) using Linear Regression in Python.

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📖 Description

This project focuses on predicting daily rainfall levels in Austin, Texas, using historical weather data.
The dataset includes weather attributes such as temperature, humidity, dew point, visibility, wind speed, and sea-level pressure.

By applying data cleaning, preprocessing, and linear regression modeling, the project demonstrates how different weather parameters influence precipitation.
The goal is not only to predict rainfall amounts but also to visualize and understand trends through correlation heatmaps, scatter plots, and actual vs predicted comparisons.

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📌 Project Overview

The objective of this project is to:

• Clean and preprocess the Austin weather dataset
• Handle missing values and special symbols like "T" (trace) and "-"
• Explore weather features such as temperature, humidity, visibility, and wind
• Build a Linear Regression model to predict precipitation
• Evaluate the model with metrics (RMSE and R² score)
• Visualize data trends and model results

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📊 Dataset

• Source: Austin Weather dataset (CSV)
• Features:
  • Temperature (High, Avg, Low)
  • Humidity (High, Avg, Low)
  • Dew Point
  • Visibility (High, Avg, Low)
  • Wind (High, Avg, Gust)
  • Sea Level Pressure
  • Precipitation (target variable)

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⚙️ Steps Performed

1. Data Loading & Inspection
2. Data Cleaning & Preprocessing
   • Removed irrelevant columns (Date, Events)
   • Handled missing values & replaced "T" with 0
   • Converted all features to numeric
3. Feature Selection – Used all relevant numeric features
4. Train-Test Split – 80% training, 20% testing
5. Model Training – Linear Regression with scikit-learn
6. Evaluation – RMSE and R² metrics
7. Visualization – Correlation heatmap, scatter plots, Actual vs Predicted plot

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📈 Results

• Model Evaluation:

  • RMSE (Root Mean Squared Error)
  • R² Score (explained variance of precipitation prediction)

• Insights:

  • Precipitation increases with humidity
  • Lower visibility often correlates with rainfall
  • Predictions align fairly well with actual precipitation values

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🛠️ Tech Stack

• Python
• Pandas, NumPy – Data handling
• Matplotlib, Seaborn – Visualization
• Scikit-learn – Machine learning model

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🚀 How to Run

1. Clone this repository

   git clone https://github.com/erharsh2104/rainfall-prediction.git
   cd rainfall-prediction
   

jupyter notebook Rainfall_Prediction_Project.ipynb

✨ Author

👨‍💻 Harsh Tripathi

Engineering Student @ Indian Institute of Information Technology, Raichur, Karnataka

📧 tripathiharsh2104@gmail.com