NovaNector_ML_Intern

Elementary Project 1

Housing Price Prediction Project

1. Introduction This documentation provides details on the housing price prediction model, including its architecture, training process, and usage instructions. The goal of my project is to predict housing prices based on various features, using different machine-learning models.

2. Model Architecture

Models Used

• Linear Regression
• Decision Tree Regressor
• Random Forest Regressor

Each model has its own architecture and approach to prediction:

I. Linear Regression: This model predicts the target variable by fitting a linear relationship between the input features and the target.

II. Decision Tree Regressor: This model splits the data into subsets based on the value of input features, creating a tree-like model of decisions.

III. Random Forest Regressor: This ensemble model combines multiple decision trees to improve prediction accuracy and control over-fitting.

3. Training Process

a. Data Collection and Importing:

• Import the dataset into a Jupyter notebook using pandas.
• Example:

  import pandas as pd
  data = pd.read_csv('housing_data.csv')

b. Data Exploration:

• Analyze the dataset to find promising attributes and correlations.
• Plot graphs to visualize data relationships using libraries like matplotlib and seaborn.

  import matplotlib.pyplot as plt
  import seaborn as sns
  
  sns.pairplot(data)
  plt.show()

c. Data Preprocessing:

• Handle missing values, encode categorical variables, and standardize the data.
• Create a pipeline to automate these steps.

  from sklearn.pipeline import Pipeline
  from sklearn.impute import SimpleImputer
  from sklearn.preprocessing import StandardScaler
  
  pipeline = Pipeline([
      ('imputer', SimpleImputer(strategy='median')),
      ('scaler', StandardScaler())
  ])
  
  data_prepared = pipeline.fit_transform(data)

d. Train-Test Splitting:

• Split the dataset into training and testing sets to evaluate the model performance.

  from sklearn.model_selection import train_test_split
  
  train_set, test_set = train_test_split(data, test_size=0.2, random_state=42)

e. Model Training:

• Train the models using the training set and evaluate them using cross-validation.

  from sklearn.linear_model import LinearRegression
  from sklearn.tree import DecisionTreeRegressor
  from sklearn.ensemble import RandomForestRegressor
  from sklearn.model_selection import cross_val_score
  
  lin_reg = LinearRegression()
  lin_reg.fit(train_set, train_set_labels)
  
  tree_reg = DecisionTreeRegressor()
  tree_reg.fit(train_set, train_set_labels)
  
  forest_reg = RandomForestRegressor()
  forest_reg.fit(train_set, train_set_labels)

f. Model Evaluation:

• Evaluate the models using cross-validation and metrics like RMSE (Root Mean Square Error).

  from sklearn.metrics import mean_squared_error
  import numpy as np
  
  lin_scores = cross_val_score(lin_reg, train_set, train_set_labels, scoring='neg_mean_squared_error', cv=10)
  lin_rmse_scores = np.sqrt(-lin_scores)
  
  def display_scores(scores):
      print("Scores:", scores)
      print("Mean:", scores.mean())
      print("Standard deviation:", scores.std())
  
  display_scores(lin_rmse_scores)

3. Usage Instructions

Requirements

• Python 3.x
• Jupyter Notebook
• Libraries: pandas, numpy, matplotlib, seaborn, scikit-learn

** How to Use

• Ensure you have the notebook (housing_price_prediction.ipynb) and data file (housing_data.csv).
• Navigate to the project directory and open the Jupyter notebook.

  jupyter notebook housing_price_prediction.ipynb

• Execute the cells step by step to load data, preprocess it, train the models, and evaluate them.

4. Predict Housing Prices:

   • Use the trained model to predict housing prices for new data.

     final_model = forest_reg
     some_data = data.iloc[:5]
     some_labels = labels.iloc[:5]
     prepared_data = pipeline.transform(some_data)
     predictions = final_model.predict(prepared_data)

5. Conclusion

This project demonstrates the complete workflow for solving a machine learning problem, from data collection to model deployment.