Effort-Estimation-Model

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Effort-Estimation-Model

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This project explores CO₂ geosequestration in depleted hydrocarbon reservoirs, investigating caprock integrity and the potential role of impurities in enhancing self-sealing mechanisms, contributing to safe, long-term carbon storage and global net-zero climate goals.

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# Effort Estimation Model

A machine learning model for predicting project effort based on various project characteristics, using synthetic data.

## Table of Contents
- [Overview](#overview)
- [Features](#features)
- [Installation](#installation)
- [Usage](#usage)
- [Data Generation](#data-generation)
- [Model Details](#model-details)
- [Evaluation Metrics](#evaluation-metrics)
- [Example](#example)
- [License](#license)

## Overview

This project implements a Random Forest regression model to estimate project effort (in person-days) based on synthetic project data. The model takes into account factors like project size, team experience, technical complexity, and development methodology.

## Features

- Synthetic data generation with customizable parameters
- Exploratory data analysis visualizations
- Random Forest regression model
- Feature importance analysis
- Model evaluation metrics
- Example prediction capability

## Installation

1. Clone this repository:
   ```bash
   git clone https://github.com/Akajiaku1/effort-estimation-model.git
   cd effort-estimation-model

Create and activate a virtual environment (recommended):
    bash
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python -m venv venv
    source venv/bin/activate  # On Windows use `venv\Scripts\activate`

Install the required packages:
    bash
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pip install -r requirements.txt

Or install them manually:
    bash
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pip install numpy pandas scikit-learn matplotlib seaborn

Usage

Run the main script:
bash
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python effort_estimation.py

This will:

Generate synthetic project data

Train the effort estimation model

Evaluate the model performance

Show feature importance

Make an example prediction

Data Generation

The synthetic data includes these features:
Feature	Description	Range/Values
project_size	Project size in function/story points	Log-normal distribution
team_experience	Average team experience in years	1-10 years
requirements_volatility	Requirements stability	1-5 scale
technical_complexity	Technical difficulty	1-5 scale
team_size	Number of team members	2-10 people
methodology	Development methodology	1=Waterfall, 2=Agile, 3=Hybrid
actual_effort	Actual effort in person-days	20-200 days
Model Details

Algorithm: Random Forest Regressor

Hyperparameters:

n_estimators: 100

random_state: 42

Input Features: All features except actual_effort

Target Variable: actual_effort

Evaluation Metrics

The model is evaluated using:

Mean Absolute Error (MAE): Average absolute difference between predictions and actual values

R-squared (R²): Proportion of variance in the dependent variable that's predictable

Typical performance on synthetic data:

MAE: ~8-12 person-days

R²: ~0.85-0.95

Author Name: Anslem Otutu
Github: https://github.com/Otutu11

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