10_ACADAMY_AIM

This repository contains the solutions and deliverables for the Week 0 challenge of the 10 Academy Artificial Intelligence Mastery (AIM) program. The challenge focuses on analyzing solar radiation data to identify high-potential regions for solar installations and present actionable insights to enhance operational efficiency and sustainability.

OBJECTIVE

. The goal of this project is to:

. Perform Exploratory Data Analysis (EDA) on solar radiation data.

. Derive insights into solar irradiance, environmental factors, and their correlations.

. Provide data-driven recommendations for solar energy investments.

. (Optional) Build an interactive Streamlit dashboard for visualizing insights.

DATASET

The dataset consists of solar radiation and environmental metrics collected from regions in Benin, Sierra Leone, and Togo. Key columns include:

. Timestamp: Date and time of observations.

. Solar Irradiance Metrics: GHI, DNI, DHI (Global, Direct, and Diffuse Horizontal Irradiance).

.Environmental Factors: Temperature, humidity, wind speed/direction, and barometric pressure.

.Cleaning Events: Indicates maintenance activities for sensors.

.Precipitation: Measured in mm/min.

SETUP INSTRUCTION

1. Clone the repositery

git clone (https://github.com/amani387/10_ACADAMY_AIM.git)

cd 10_ACADAMY_AIM

2. Set Up Python Environment

pip install -r requirements.txt

3. (Optional) Launch the Streamlit Dashboard

streamlit run app/main.pY

TASKS AND DELIVERABLES

Task 1: Exploratory Data Analysis

Perform EDA on the dataset to:

. Summarize statistics (mean, median, std. dev.).

. Visualize trends and correlations.

. Identify anomalies and outliers.

. Clean and preprocess data.

Bonus Task: Streamlit Dashboard

. Create an interactive dashboard for data exploration and visualization.

. Deploy the dashboard on Streamlit Community Cloud.

KEY FEATURES

. Descriptive Statistics: Summary of solar irradiance and environmental metrics.

. Time-Series Analysis: Trends in solar radiation over time.

. Correlation Analysis: Relationships between environmental factors and solar irradiance.

. Interactive Visualizations: User-friendly Streamlit interface.

TECHNOLOGIES USED

. Python: Data analysis and preprocessing.

. Pandas & NumPy: Data manipulation.

. Matplotlib & Seaborn: Data visualization.

. Streamlit: Interactive dashboard.

. Git & GitHub: Version control and collaboration.

RESULTS

. Link to Final Report

. (Optional) Live Streamlit App

CONTRIBUTER

Amanuel Nega - GitHub | www.linkedin.com/in/amanuel-nega-533353246