Housing Price Prediction ML

Description:

This repository contains a machine learning project for predicting house prices in California. The model is trained on the California housing dataset and aims to estimate house prices based on various features such as location, median income, and house characteristics. The project demonstrates data preprocessing, feature engineering, and model training in Python using Jupyter Notebook.

Features:

• Data Preprocessing

  • Handling missing values
  • Feature scaling and encoding
  • Exploratory Data Analysis (EDA) with visualizations

• Feature Engineering

  • Selecting relevant features for prediction
  • Handling categorical and numerical data

• Model Training

  • Multiple regression models (e.g., Linear Regression, Decision Tree, Random Forest)
  • Hyperparameter tuning to optimize model performance

• Evaluation Metrics

  • Mean Squared Error (MSE)
  • Root Mean Squared Error (RMSE)
  • R-squared score

• Predictions

  • Generating predictions for new housing data inputs

Requirements:

Ensure you have the following software and libraries installed:

1. Software

   • Python 3.8 or above
   • Jupyter Notebook

2. Python Libraries

   • NumPy
   • pandas
   • scikit-learn
   • Matplotlib
   • seaborn

Instructions:

1. Clone the Repository

   bash

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   git clone https://github.com/yourusername/california-housing-prediction.git cd california-housing-prediction

2. Install the Dependencies
   Ensure all required Python libraries are installed using pip:

   bash

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   pip install -r requirements.txt

3. Open the Jupyter Notebook
   Start Jupyter Notebook and open the project file:

   bash

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   jupyter notebook

4. Run the Notebook

   • Load the housing dataset.
   • Follow the steps in the notebook to preprocess data, train models, and evaluate predictions.

5. Make Predictions
   Modify the input features in the provided prediction cells to estimate house prices.

6. Customize and Extend

   • Experiment with different models or parameters.
   • Add visualizations to improve data insights.