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Feature_selection.py
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Feature_selection.py
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#!/usr/bin/env python
# coding: utf-8
# # Linear Regression Continuation
# In[1]:
import pandas as pd
from sklearn.datasets import load_boston,load_wine
# In[2]:
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
get_ipython().run_line_magic('matplotlib', 'inline')
# In[3]:
sns.set(color_codes=True)
sns.set(rc={'figure.figsize':(10,10)})
# In[4]:
boston_dataset = load_boston()
# In[5]:
boston_data = pd.DataFrame(boston_dataset.data,columns=boston_dataset.feature_names)
# In[6]:
boston_data['MEDV']=boston_dataset.target
# In[7]:
boston_data.head(5)
# # Feature Selection
# - Finding and training the model on relevant data becomes important because if trained on completely or partially irrelevent features,the model won't give accuaracy.
# - selecting features wisely to train the model, we improve the accuaracy,reduce the overfitting and will not get misleaded by redundant data.
# In[8]:
boston_correlation = boston_data.corr().round(2)
# In[9]:
top_core_features = boston_correlation.index
# In[10]:
sns.heatmap(boston_correlation,annot=True,cmap='RdYlGn')
# In[11]:
sns.heatmap(boston_correlation[top_core_features].corr(),annot=True,cmap='RdYlGn')
# > - As LSTAT decreases the MEDV increases,as RM increases the MEDV increses too
# # Scatter Plots
# > - Scatter Plots are used to observe relationship between 2 variables.
# In[12]:
boston_data.plot(
kind='scatter',
x='RM',
y='MEDV',
color='green'
)
# > The price increases as the number of rooms increases.
# In[13]:
boston_data.plot(
kind='scatter',
x='LSTAT',
y='MEDV',
color='red',
label=''
)
# > The prices decreases as the LSTAT increases <br> So,we know that the better feature to predict about MEDV with future data is this feature RM <br> So,the line equation becomes something like y= m*(RM) + c
# In[14]:
X = boston_data[['RM']]
# In[15]:
Y = boston_data['MEDV']
# In[16]:
X.shape
# In[17]:
Y.shape
# In[18]:
boston_data['MEDV'].describe()
# # Model Instantiation
# In[19]:
from sklearn.linear_model import LinearRegression
# In[20]:
model = LinearRegression()
# In[21]:
from sklearn.model_selection import train_test_split
# In[22]:
X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.3,random_state=1)
# In[23]:
X_train.shape
# In[24]:
X_test.shape
# In[25]:
Y_train.shape
# In[26]:
Y_test.shape
# # Fitting the model
# In[27]:
model.fit(X_train,Y_train)
# # Estimating the Parameters
# In[28]:
model.intercept_
# In[29]:
model.intercept_.round(2)
# In[30]:
model.coef_
# In[31]:
model.coef_.round(2)
# Therefore, MEDV = -30.57 + 8.46*(RM)
# # Prediction
# In[32]:
y_test_prediction = model.predict(X_test)
# In[33]:
y_test_prediction.shape
# In[34]:
type(y_test_prediction)
# In[35]:
plt.scatter(
X_test,
Y_test,
label='Testing The Model',
)
plt.plot(
X_test,
y_test_prediction,
label='Prediction',
linewidth=2,
)
plt.xlabel='RM'
plt.ylabel='MEDV'
plt.legend(loc='upper left')
plt.show()
# # Evaluating the Model
# In[36]:
residual = Y_test - y_test_prediction
# In[37]:
plt.scatter(X_test,residual)
plt.hlines(y=0,xmin=X_test.min(),xmax=X_test.max(),linestyle='--')
xlim=(4,9)
plt.show()
# In[38]:
residual[:5].round(3)
# In[39]:
(residual**2).mean()
# In[40]:
from sklearn.metrics import mean_squared_error
# In[41]:
mean_squared_error(Y_test,y_test_prediction)
# # R Squared
# In[42]:
model.score(X_test,Y_test)
# # Multivarient Linear Regression
# In[43]:
X2 = boston_data[['RM','LSTAT']]
# In[44]:
Y2 = boston_data['MEDV']
# In[45]:
X2_train,X2_test,Y2_train,Y2_test = train_test_split(X2,Y2,test_size=0.3,random_state=1)
# In[46]:
model2= LinearRegression()
# In[47]:
model2.fit(X2_train,Y2_train)
# In[48]:
model2.intercept_
# In[49]:
model2.coef_
# In[50]:
y2_test_prediction = model2.predict(X2_test)
# # Evaluating the model again
# # Mean Squared Error
# In[51]:
mean_squared_error(Y2_test,y2_test_prediction)
# In[52]:
model2.score(X2_test,Y2_test)