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# Weather-Prediction-Device
Developed ML model and deployed on esp32 which uses DHT11 sensor to get temperature and humidity with this it calculates heat index and then predicts weather condition.
# **Introduction to Machine Learning with Jupyter Notebooks**
Welcome to the world of Machine Learning. This instructional materials will guide you to learn machine learning using Jupyter Notebook.
## **Getting Started**
### **1. Setting Up Your Environment**
Before we go deep into machine learning, you'll need to set up your environment.
follow these steps to set up your environment.
**Install Jupyter Notebook**:
Install the classic Jupyter Notebook by writting the below command in your terminal:
`pip install notebook`
### **2. Launching Jupyter Notebook**
To run the notebook:
`jupyter notebook`
or if this doesnt work, write this insteade.
`python -m notebook`
## **Basics of Machain Learning (ML)**
### **What is Machine Learning?**
It is a subset of Artificial Inteligence (AI). It focuses on making a model capable of learning from data to make predictions or decisions.
### **Types of Machaine Learning:**
1. *Supervised Learning*: Learning from labeled data (e.g., classification, regression).
2. *Unsupervised Learning*: Finding patterns in unlabeled data (e.g., clustering, dimensionality reduction).
3. *Reinforcement Learning*: Learning through rewards and punishments.
## **Building your first ML Model**
**Task**: Develop a weather forecasting model capable of predicting
weather conditions, including sunny, cloudy, rainy, foggy, etc.
**Objective**:
Develop instructional materials for students to learn machine learning using
Jupyter Notebook and other ML tools. Use a weather forecasting model as an
example, incorporating TensorFlow and a Decision Tree Classifier. The aim is
to accurately predict weather conditions and display predictions such as
sunny, cloudy, rainy, and foggy.
**Tools and Technologies**:
1. *Jupyter Notebook*: For data preprocessing, model development, and
training.
2. *Programming Language*: Python - Widely used for its simplicity,
extensive libraries, and ecosystem support.
3. *TensorFlow*: For building and training the weather prediction model.
4. *NumPy*: For numerical computations and data manipulation.
5. *Pandas*: For data manipulation and analysis.
## Developed by Harsh Raj
Contacts :
- [Email](mailto:developerharshraj@gmail.com)
- [LinkedIn](https://in.linkedin.com/in/harsh-raj-416a0b27b)
- [GitHub](https://github.com/HarshRajTiwary)
## Happy Learning
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