Weather Prediction Model

Overview

This repository contains a Python project developed by Anish Kumar for predicting weather using data analysis and machine learning neural networks. The project reads data from a CSV file, processes it, trains a neural network, and visualizes the results.

Table of Contents

1. Introduction
2. Data Source
3. Libraries Used
4. Workflow
5. Data Preprocessing
6. Model Training
7. Prediction
8. Visualization
9. Conclusion

Introduction

The primary function of this Python code is to analyze weather data from a CSV file and predict future weather conditions using Convolutional Neural Networks (CNN). The results are then visualized using matplotlib.

Data Source

• CSV File: L A_Weather.csv

Libraries Used

• Data Manipulation and Analysis: os , numpy , pandas , sklearn
• Machine Learning Frameworks: tensorflow , keras
• Visualization: matplotlib

Workflow

1. Loading the CSV file
2. Data Preprocessing and Cleaning
3. Training the Neural Network
4. Predicting Weather
5. Visualizing the Results

Data Preprocessing

Describe the steps taken to preprocess and clean the data. Include any specific techniques or transformations applied to the dataset.

Model Training

Detail the process of training the Convolutional Neural Network (CNN). Include information about the architecture of the neural network, the training parameters, and any validation techniques used.

Prediction

Explain how the trained model is used to predict weather conditions. Include any post-processing steps applied to the predictions.

Visualization

Describe how the results are visualized using matplotlib. Include examples of the types of plots generated and their significance.