SkillCraft Technology ML Internship - Task 1

House Price Prediction Model

📌 Project Overview

This project implements a linear regression model to predict the prices of houses based on key features such as square footage, the number of bedrooms, and the number of bathrooms. This was completed as part of my Machine Learning internship at SkillCraft Technology.

📊 Dataset

The model is trained on the house price.csv dataset, which contains the following key variables:

• Square Footage: The total living area of the house.
• Bedrooms: The number of bedrooms in the house.
• Bathrooms: The number of bathrooms in the house.
• Price: The target variable representing the market value of the house.

🛠️ Technologies Used

• Language: Python
• Libraries:
  • pandas & numpy (Data manipulation and cleaning)
  • matplotlib & seaborn (Data visualization)
  • scikit-learn (Model training and evaluation)

📈 Implementation Steps

1. Data Preprocessing: Handled missing values, explored data distributions, and filtered out anomalies.
2. Feature Selection: Split the dataset into features (independent variables) and the target price (dependent variable).
3. Model Training: Built and trained a Multiple Linear Regression model using scikit-learn.
4. Evaluation: Evaluated model performance using standard regression metrics.

🚀 How to Run This Project

1. Clone this repository to your local machine.
2. Ensure you have the required dependencies installed:

   pip install pandas numpy scikit-learn matplotlib seaborn notebook

3. Open the Jupyter Notebook:

   jupyter notebook Task1.ipynb

4. Run all cells to see the data analysis, visualizations, and model training results.