💼 Freelancer Gig Price Prediction

This project focuses on predicting freelancer earnings based on job-related features such as category, experience level, country, and other factors. Using data preprocessing and machine learning techniques, the goal is to estimate the potential earnings (USD) for freelancers.

📊 Project Overview

• Dataset: freelancer_earnings_bd.csv
• Target Variable: Earnings_USD
• Features: Job Category, Experience Level, Location, Skills, etc.
• Objective: Build a regression model that predicts freelancer earnings to provide insights into the freelance economy.

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⚙️ Steps Performed

Data Loading & Exploration

• Loaded dataset using Pandas
• Inspected structure, missing values, and summary statistics

Data Cleaning & Preprocessing

• Dropped irrelevant columns (Freelancer_ID)
• Encoded categorical variables using LabelEncoder
• Standardized numerical features with StandardScaler

Exploratory Data Analysis (EDA)

• Distribution of earnings by experience level
• Correlation heatmap between numerical features

Model Building

• Train-test split
• Random Forest Regressor for prediction
• (Future scope: try XGBoost, Linear Regression, etc.)

Evaluation

• Metrics: R² Score, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE)

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🛠️ Installation & Setup

Clone the repository:

git clone https://github.com/your-username/Freelancer-Price-Prediction.git
cd Freelancer-Price-Prediction

Install dependencies:

pip install -r requirements.txt

Run the notebook:

jupyter notebook

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📦 Requirements

The project uses the following Python libraries:

pandas
numpy
matplotlib
seaborn
scikit-learn
gradio

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🚀 Future Enhancements

• Add XGBoost / LightGBM models for comparison
• Deploy the model with Gradio / Streamlit
• Hyperparameter tuning with GridSearchCV
• Visual dashboard for better interpretability