project-3: Supervised Learning (Linear Regression)

Overview

This project applies supervised learning techniques, specifically linear regression, to analyze used device data. The goal is to build a predictive model that can estimate the value of used devices based on their features.

Project Structure

• Supervised learning-Linear_regression.ipynb: Jupyter Notebook containing detailed exploratory data analysis, data preprocessing, and linear regression implementation steps.
• used_device_data.csv: Dataset containing information about used devices (e.g., specifications, condition, and price).
• README.md: Documentation on project purpose, requirements, and usage guidelines.

Getting Started

1. Install dependencies:

   • Python 3.x
   • pandas, numpy, matplotlib, seaborn, scikit-learn

2. Set up environment (example with pip):

   pip install pandas numpy matplotlib seaborn scikit-learn

3. Run the Notebook:
   1. Open the Jupyter Notebook (Supervised learning-Linear_regression.ipynb).
   2. Restart the kernel and run all cells sequentially.

Methodology

1. Data Loading: Reads and cleans the used device dataset.
2. Exploratory Analysis: Uses summary statistics and visualizations to understand data distribution and relationships.
3. Preprocessing: Handles missing values, encodes categorical variables, and scales numerical features.
4. Linear Regression: Builds and evaluates a regression model to predict device prices.

Results & Insights

• The linear regression model identifies key factors influencing the price of used devices.
• Insights from the analysis can help sellers price their devices competitively and buyers make informed decisions.

Contributing

Contributions in the form of issue reports or suggestions are welcome.