house pricing.py

#!/usr/bin/env python
# coding: utf-8

# ## Project: Predicting Boston Housing Prices
# ### Data  
# 
# The modified Boston housing dataset consists of 489 data points, with each datapoint having 3 features. This dataset is a modified version of the Boston Housing dataset found on the <a href="https://archive.ics.uci.edu/ml/index.php">UCI Machine Learning Repository</a> and you can find the main dataset on the 
# <a href="https://www.kaggle.com/c/boston-housing">Kaggle</a>
# 
# 
# ### Features  
# 
# RM: average number of rooms per dwelling (Total number of rooms in home)  
# LSTAT: percentage of population considered lower status (Neighborhood poverty level )  
# PTRATIO: pupil-teacher ratio by town  (Student-teacher ratio of nearby schools)  
# Target Variable: MEDV: median value of owner-occupied homes (house price)  

# In[13]:


# Import libraries:
import numpy as np
import pandas as pd
get_ipython().run_line_magic('matplotlib', 'inline')


# In[16]:


# Load the Boston housing dataset
data = pd.read_csv('housing.csv')
data.info()
data.head(10)


# In[17]:


# Data Exploration
data.plot.scatter('RM','MEDV',c='r');
data.plot.scatter('LSTAT','MEDV',c='c');
data.plot.scatter('PTRATIO','MEDV',c='g');




# In[18]:


#define variables(features,prices)
prices = data['MEDV']

features = data.drop('MEDV', axis = 1)
features.head(5)    


# In[19]:


#split the data to two sets. training set and testing set:
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(features, prices, test_size = 0.25)


# In[20]:


print("training set:",X_train.shape,y_train.shape)
print("testing set:",X_test.shape,y_test.shape[0])


# In[21]:


#create the rgression model:
from sklearn.linear_model import LinearRegression
model = LinearRegression()


# In[22]:


#fit/train the model:
model.fit(X_train,y_train);


# In[23]:


#predict X_test by the model:
y_pred=model.predict(X_test)


# In[24]:


#model accuracy test:
from sklearn.metrics import r2_score
r2_score(y_test, y_pred)


# In[25]:


#extra point: model accuracy train: to check underfitting and oferfitting
y_train_pre=model.predict(X_train)
r2_score(y_train, y_train_pre)


# In[ ]:





# ## Extra:  
# prdict the price for house with:   
# Total number of rooms in home =7 rooms  
# Neighborhood poverty level as 20%  
# Student-teacher ratio of nearby schools=19-to-1  
# 

# In[28]:


np.array([7,20,19]).reshape(1,-1).shape


# In[29]:


model.predict(np.array([7,20,19]).reshape(1,-1))


# In[ ]:





# In[ ]: