lowbhattery/Ghana-Port-Vessel-Call-Traffic

📊 Tema Port Vessel Calls Prediction Author: Nana Akwasi Adjei Odoom Date: January 2026 Project Type: Predictive Analytics / Time-Series Forecasting Programming Language: Python Libraries: pandas, scikit-learn ## 📄 Data Disclaimer The dataset used in this project was obtained from publicly available online sources. While every effort was made to ensure accuracy and consistency, minor discrepancies may exist due to reporting methods and data aggregation across different years. 📑 Table of Contents Project Overview Dataset Data Cleaning Exploratory Data Analysis Predictive Modeling Results Conclusion Future Work Usage Instructions License 📌 Project Overview This project focuses on predicting annual vessel traffic at Tema Port, Ghana, using historical vessel call data from 2000 to 2024. The main objectives are to: Analyze historical trends in vessel calls Predict future vessel traffic (2025–2030) Provide data-driven insights to support port planning and management decisions 📂 Dataset Source: Publicly available online data from the Ghana Ports and Harbours Authority (GPHA) and related maritime traffic publications. The dataset represents real historical annual vessel call records for Tema Port and was compiled from official online sources for analytical and academic purposes. Columns: id → Unique identifier (not used for modeling) Year → Independent variable Calls → Dependent variable (number of vessel calls) Sample Data: id Year Calls 1 2000 1163 2 2001 1169 3 2002 1170 … … … 🧹 Data Cleaning The following steps were applied to ensure data quality: Converted Year and Calls to numeric data types Interpolated missing values in Calls to preserve the trend Removed invalid or non-finite Year values Rounded Calls to integers for consistency import pandas as pd import numpy as np df = pd.read_csv("tema_port.csv").copy() df['YEAR'] = pd.to_numeric(df['YEAR'], errors='coerce') df = df.dropna(subset=['YEAR']) df['YEAR'] = df['YEAR'].astype(int) …