Omdena-CBP-Tanzania/omdena-cbp-tanzania-classroom-d4cad3-capstone-project-Capstone-Project

omdena-cbp-tanzania-classroom-d4cad3-capstone-project-Capstone-Project created by GitHub Classroom # Project: **Climate Change Analysis in Tanzania** ## Objective: - Analyzing historical climate data to understand trends and predict future climate patterns in Tanzania. ### Tools and Libraries: - Python (Pandas, NumPy, Matplotlib, Seaborn) - Scikit-learn (for Machine Learning) - Streamlit (for deployment) ## Project Structure: ### 1. Data Collection: - *Source*: Use publicly available climate datasets (e.g., NOAA, World Bank Climate Data) that include historical weather patterns in Tanzania. - Download and integrate the data to a csv file that you can use further for the analysis. - Data Format: CSV or Excel files are commonly available formats. - Output: Downloaded dataset in a structured format ready for preprocessing. ### 2. Data Preprocessing: Tasks: - Handle missing values (if any). - Convert data types as necessary (e.g., datetime conversion). - Feature engineering: Extract relevant features such as seasonal trends, average temperatures, precipitation levels. - Encoding categorical variables (if applicable). - Output: Cleaned dataset ready for exploratory data analysis (EDA) and modeling. ### 3. Exploratory Data Analysis (EDA): Tasks: - Statistical summaries: Descriptive statistics (mean, median, variance). - Data visualization: Plot time series of temperature trends, precipitation levels over the years. - Identify correlations: Heatmaps, scatter plots to understand relationships between variables. - Seasonal decomposition: Identify seasonal patterns using decomposition techniques (e.g., using seasonal_decompose from statsmodels). - Output: Visualizations (line plots, histograms, heatmaps) depicting historical climate trends and patterns. ### 4. Machine Learning Model Development: ##### Objective: Predict future climate conditions based on historical data. Tasks: - Split data into training and testing sets. - Select appropriate ML model(s) (e.g., Linear Regression, Random Forest) …