🏠 Perceptron for House Price Prediction

This project focuses on applying machine learning techniques—including a custom-built Perceptron—to analyze and predict house prices using the California Housing Dataset. It includes data preprocessing, exploratory visualization, and the application of several models: a custom Perceptron, linear regression, and a deep neural network built with Keras.

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📁 Dataset

The dataset (housing.csv) includes information on California housing blocks such as:

• Median income
• House age
• Total rooms
• Population
• Median house value (target)
• And more, including a categorical feature (ocean_proximity)

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📊 Features of the Project

1. Data Exploration and Visualization

• Dataset structure, null value handling, and statistical summaries
• Histograms and KDE plots for feature distribution
• Scatter plots showing feature relationships and geographic clustering

2. Preprocessing Steps

• Handling missing values (total_bedrooms filled with median)
• Feature scaling using:
  • MinMaxScaler
  • StandardScaler
  • MaxAbsScaler
  • RobustScaler
  • Normalizer
• One-Hot Encoding for categorical variables

3. Machine Learning Models

🧠 Custom Perceptron

• Implemented from scratch
• Trained using k-fold cross-validation
• Evaluated using:
  • Classification Accuracy
  • Mean Squared Error (MSE)
  • Mean Absolute Error (MAE)

📈 Linear Regression (from scratch)

• Univariate regression using 10-fold CV
• Manually calculated slope/intercept
• Metrics:
  • MSE
  • MAE
• Final predictions plotted for visual inspection

🤖 Deep Neural Network (Keras)

• Multilayer feedforward neural network
• Layers: Dense(128) → Dense(32) → Dense(8) → Dense(1)
• Loss: Mean Squared Error
• Optimizer: Adam
• Early stopping to avoid overfitting
• R² score printed for validation performance

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📦 Requirements

Install the necessary packages using pip:

pip install pandas numpy matplotlib seaborn scikit-learn keras tensorflow