🌦️ Weather Forecast Dashboard (Streamlit)

🧭 Project Overview

An interactive Streamlit dashboard that visualises temperature data collected from multiple campus areas, enabling quick comparison through charts and dashboard summaries.

🧠 Key Features

1. 📊 Interactive Dashboard – Displays temperature data in charts and tables.
2. 🧩 Multiple Visualization Modes – Users can navigate between Dashboard, Pie Chart, Bar Chart, and Area views.
3. 🌍 Area-Based Analysis – Temperature readings categorized by campus zones: a. SPCAI b. A2 Block c. STC d. Hostel
4. 📈 CSV Data Processing – Reads data from CSV files and visualizes results dynamically.
5. 🧭 User-Friendly Navigation – Sidebar with navigation tabs for effortless exploration.

🧩 Project Workflow

1. Data Collection: Temperature readings are collected through sensors installed in four university areas.

2. Data Storage: The readings are stored in a structured CSV file, containing records of temperature values for each area.

3. Data Visualization: Using Streamlit and Pandas, the dashboard processes the CSV data and presents it through:

   1. Bar charts for area-wise comparisons
   2. Pie charts for distribution analysis
   3. Dashboard summary for overall insights

4. Analysis: Users can easily identify temperature variations and compare readings across all areas.

🏗️ System Architecture

• Data layer: CSV file(s) (e.g., Weather_Forecast/sensor_data.csv)
• Processing: Python modules for reading/transforming data
• Visualisation: Streamlit UI + charts (Charts.py)
• Navigation: Sidebar/menu routing via Menu.py

Architecture flow (text):
Sensor CSV → Python Processing → Streamlit UI → Charts/Insights

🧰 Technologies & Tools

Languages & Libraries: 'Python', 'Streamlit', 'Pandas', 'Matplotlib' Development & Deployment Tools: 'VS Code', 'GitHub' Data Handling: 'CSV' 'Kaggle Datasets'

⚙️ Installation and Setup

1) Clone

git clone https://github.com/HamnaIqbal44/Weather-Forecast-Dashboard.git
cd Weather-Forecast-Dashboard

2) Create and activate a virtual environment

python -m venv .venv

Windows

.venv\Scripts\activate

macOS/Linux

source .venv/bin/activate

3) Install dependencies

This repository currently does not include a requirements.txt.
Option A (recommended): create requirements.txt and install from it.
Option B (quick start): install minimal dependencies directly.

Option B (quick start)

pip install streamlit pandas plotly

4) Run the Streamlit app (actual entrypoint)

streamlit run Weather_Forecast/main.py

🗃️ Dataset

• Primary dataset file: Weather_Forecast/sensor_data.csv

🧪 Usage / Demo

1. Run the Streamlit app.
2. Use the sidebar/menu to switch views.
3. Compare area-wise temperature patterns and trends.

👩‍💻 Author

Hamna Iqbal