NSHUTI-5/rwanda-agriculture-indicators-dashboard

# 🌾 Rwanda Agriculture Development Analytics | INSY 8413 Capstone Project ## 📘 Project Overview This project explores agricultural development trends in Rwanda using data analytics. By analyzing key indicators such as land use, crop production, and rural development metrics, the goal is to uncover patterns and insights that can support more informed agricultural planning. The project uses Python for data preprocessing, visualization, and clustering, and Power BI for interactive dashboards. This combination provides both deep analytical insight and user-friendly visual storytelling. --- ## MY Information - **Name: Nshuti Dorothy and ID:27129 - **Sector: Agriculture - **Course: Introduction to Big Data Analytics - **Instructor: Eric Maniraguha # Problem Statement "Can access to irrigation influence yields across Rwanda’s main staple crops?" Dataset Information - Title: Agriculture and Rural Development – Rwanda - Source: Humanitarian Data Exchange (HDX) - **Dataset Link:** Download CSV - **Colab link:** Reach on my google colab link - Format: CSV - Size: 60 rows × 6 columns ## 🧪 Python Analytics Tasks In this section, we used Python to clean and explore the dataset, apply clustering techniques, and evaluate the results. The goal was to uncover patterns in agricultural data and support better decision-making. ### ✅ 1. Data Cleaning - Handle missing values - Fill missing values (forward fill, backward fill) - Standardize data types - Detect and remove outliers using IQR - Apply scaling # Categorical/text columns: fill with most common cat_cols = df.select_dtypes(include=['object']).columns for c in cat_cols: df[c] = df[c].fillna(df[c].mode()[0]) ### ✅ 2. Exploratory Data Analysis (EDA) - Descriptive statistics (`df.describe()`) - Correlation matrix (`sns.heatmap`) - Time series trends - Histograms, boxplots, and pairplots screenshots of visualizations in: enshots/eda/` ### ✅ 3. Machine Learning – Clustering - Chosen Model: K-Means Clustering - Use Elb …