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This project focuses on predicting rainfall patterns using historical weather data and machine learning algorithms. The dataset was preprocessed to handle missing values, normalize features, and extract relevant parameters like temperature, humidity, and wind speed. Models such as Linear Regression and Random Forest were implemented and evaluated for accuracy. 🔹 Tools & Technologies: Python, Pandas, NumPy, Scikit-learn, Matplotlib 🔹 Key Features: 1)Data cleaning and feature engineering for reliable input. 2)Training and testing predictive models.
IoT-Based Smart Dosing System for Hydroponic Agriculture
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