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https://github.com/chandkund/housing-price-prediction

Predict housing prices using the Boston Housing Dataset. Covers data loading, cleaning, preprocessing, EDA, normalization, standardization, and regression models (Linear Regression, Decision Tree, Random Forest, Extra Trees). Evaluated with Mean Squared Error (MSE). Tech: Python, Pandas, NumPy, Scikit-learn, Seaborn, Matplotlib.
https://github.com/chandkund/housing-price-prediction

data-science data-visualization matplotlib numpy pandas pyhton sklearn sklearn-library sklearn-metrics

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Predict housing prices using the Boston Housing Dataset. Covers data loading, cleaning, preprocessing, EDA, normalization, standardization, and regression models (Linear Regression, Decision Tree, Random Forest, Extra Trees). Evaluated with Mean Squared Error (MSE). Tech: Python, Pandas, NumPy, Scikit-learn, Seaborn, Matplotlib.

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README 

        
# Housing-Price-Prediction



This repository contains a project on predicting housing prices using various machine learning algorithms. The project includes data cleaning, preprocessing, exploratory data analysis, normalization, standardization, and model building using multiple regression techniques.



## Table of Contents

- [Introduction](#introduction)

- [Installation](#installation)

- [Data_Loading](#data_loading)

- [Data_Cleaning_and_Preprocessing](#data_cleaning_and_preprocessing)

- [Exploratory_Data_Analysis](#exploratory_data_analysis)

- [Normalization_and_Standardization](#normalization_and_standardization)

- [Modeling](#modeling)

- [Results](#results)

- [License](#license)

  

## Introduction



The Boston Housing Dataset is a famous dataset derived from the Boston Census Service, originally curated by Harrison and Rubinfeld in 1978. The dataset contains information collected by the U.S. Census Service concerning housing in the area of Boston, Massachusetts.



The dataset includes 506 instances with 14 attributes or features:



- **CRIM**: per capita crime rate by town

- **ZN**: proportion of residential land zoned for lots over 25,000 sq. ft.

- **INDUS**: proportion of non-retail business acres per town

- **CHAS**: Charles River dummy variable (1 if tract bounds river; 0 otherwise)

- **NOX**: nitrogen oxides concentration (parts per 10 million)

- **RM**: average number of rooms per dwelling

- **AGE**: proportion of owner-occupied units built prior to 1940

- **DIS**: weighted distances to five Boston employment centers

- **RAD**: index of accessibility to radial highways

- **TAX**: full-value property tax rate per $10,000

- **PTRATIO**: pupil-teacher ratio by town

- **B**: 1000(Bk - 0.63)^2 where Bk is the proportion of Black residents by town

- **LSTAT**: percentage of lower status of the population

- **MEDV**: median value of owner-occupied homes in $1000s (Target Variable)



  

This dataset is particularly useful for regression tasks where the goal is to predict the median value of  owner-occupied homes (MEDV) using the other 13 attributes.



## Installation

To run this project, you need to have Python and the following libraries installed:



- `pandas`

- `numpy`

- `matplotlib`

- `seaborn`

- `scikit-learn`

  

You can install these libraries using pip:



     pip install pandas numpy scikit-learn seaborn matplotlib

  

OR 

Clone the repository and install the required libraries:



     git clone https://github.com/chandkund/Housing-Price-Prediction.git



## Data_Loading

First, load the dataset using pandas:

```python

import pandas as pd

raw_data = pd.read_csv("D:\\Data_Science_Project\\Project_2\\HousingData.csv")

df = raw_data.copy()

df.info()  # Check the dataset information

```



## Data_Cleaning_and_Preprocessing

The following steps are performed for data cleaning and preprocessing:



-  Handle missing values in numerical and categorical columns using SimpleImputer.

-  Visualize the cleaned data using box plots and histograms.

-  Normalize and standardize the data for better model performance.

-  

 ### Import relevant Libraries

 ```python

from sklearn.impute import SimpleImputer

from sklearn.preprocessing import MinMaxScaler, StandardScaler



 # Numerical and Categorical columns

 numberical_col = ['CRIM', 'ZN', 'INDUS', 'AGE','LSTAT']

 categorical_cols = ['CHAS']



 # Imputing missing values

 numberical_imputer = SimpleImputer(strategy='mean')

 df[numberical_col] = numberical_imputer.fit_transform(df[numberical_col])

 categorical_imputer = SimpleImputer(strategy='most_frequent')

 df[categorical_cols] = categorical_imputer.fit_transform(df[categorical_cols])



 # Normalization

 scaler = MinMaxScaler()

 df[['CRIM', 'ZN','TAX','B']] = scaler.fit_transform(df[['CRIM', 'ZN','TAX','B']])



 # Standardization

 scaled = StandardScaler()

 df[['CRIM', 'ZN','TAX','B']] = scaled.fit_transform(df[['CRIM', 'ZN','TAX','B']])

 ```



## Exploratory_Data_Analysis

The data is visualized using various plots to understand the distributions and relationships between variables 

 ```python

import seaborn as sns

import matplotlib.pyplot as plt



# Box plots

fig, ax = plt.subplots(ncols=7, nrows=2, figsize=(20, 10))

ax = ax.flatten()

for index, col in enumerate(df.columns):

    sns.boxplot(y=col, data=df, ax=ax[index])

plt.tight_layout(pad=0.5, w_pad=0.7, h_pad=5.0)

plt.show()



# Histograms with KDE

fig, ax = plt.subplots(ncols=7, nrows=2, figsize=(20, 10))

ax = ax.flatten()

for index, col in enumerate(df.columns):

        sns.histplot(df[col], ax=ax[index], kde=True)

        ax[index].set_title(col)

plt.tight_layout(pad=0.5, w_pad=0.7, h_pad=5.0)

plt.show()

```



## Normalization_and_Standardization

  Normalization and standardization are applied to scale the features for better model performance.

```python

from sklearn.preprocessing import MinMaxScaler, StandardScaler

# Min-Max Normalization

scaler = MinMaxScaler()

df[['CRIM', 'ZN','TAX','B']] = scaler.fit_transform(df[['CRIM', 'ZN','TAX','B']])

# Standardization

scaled = StandardScaler()

df[['CRIM', 'ZN','TAX','B']] = scaled.fit_transform(df[['CRIM', 'ZN','TAX','B']])

```

## Modeling

Different regression models are built and evaluated:



- Linear Regression

- Decision Tree Regressor

- Random Forest Regressor

- Extra Trees Regressor



### Modeing

```python

from sklearn.model_selection import train_test_split

from sklearn.linear_model import LinearRegression

from sklearn.tree import DecisionTreeRegressor

from sklearn.ensemble import RandomForestRegressor, ExtraTreesRegressor

from sklearn.metrics import mean_squared_error

# Splitting the data

inputs = df.drop(columns=['MEDV', 'RAD'])

targets = df['MEDV']

X_train, X_test, Y_train, Y_test = train_test_split(inputs, targets, test_size=0.2, random_state=42)

# Linear Regression

model_1 = LinearRegression()

model_1.fit(X_train, Y_train)

pred = model_1.predict(X_test)

mse = mean_squared_error(Y_test, pred)

print(f"Linear Regression MSE: {mse}")



# Decision Tree Regressor

model_2 = DecisionTreeRegressor()

model_2.fit(X_train, Y_train)

pred = model_2.predict(X_test)

mse = mean_squared_error(Y_test, pred)

print(f"Decision Tree MSE: {mse}")



# Random Forest Regressor

model_3 = RandomForestRegressor()

model_3.fit(X_train, Y_train)

pred = model_3.predict(X_test)

mse = mean_squared_error(Y_test, pred)

print(f"Random Forest MSE: {mse}")



# Extra Trees Regressor

model_4 = ExtraTreesRegressor()

model_4.fit(X_train, Y_train)

pred = model_4.predict(X_test)

mse = mean_squared_error(Y_test, pred)

print(f"Extra Trees MSE: {mse}")

 ```



## Results

The models are evaluated based on Mean Squared Error (MSE). Below are the MSE results for each model:



- Linear Regression MSE :26.84

- Decision Tree Regressor MSE :15.75

- Random Forest Regressor MSE :6.05

- Extra Trees Regressor MSE : 10.64



## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.