Cifar

Boston-Housing-Prices/README.md

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+## Multiple Linear-Regression!
+
+### Getting Started
+
+Whats is Regression?
+
+In statistical modeling, regression analysis is a statistical process for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables (or 'predictors').
+Regression is nothing but the Prediction.
+
+
+When there is a single independent variable we call it simple linear regression.
+when there are more than independent variables the process is known as Multiple Linear-Regression.
+
+<p align = "center">
+<a href="https://www.codecogs.com/eqnedit.php?latex=P(A/B)&space;=&space;\frac{P(B/A)P(A)}{P(B)}" target="_blank"><img src="https://miro.medium.com/max/3444/1*uLHXR8LKGDucpwUYHx3VaQ.png" /></a>
+</p>
+
+Data Type must be Numeric or Continuous.
+
+There should be One dependent variable and multiple independent variables
+The Linear relation between variables which is a relation between dependent and independent variables.
+
+

Boston-Housing-Prices/boston-house-prices.py

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+
+import numpy as np
+import pandas as pd
+import sklearn
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+from sklearn import metrics
+
+
+from sklearn.datasets import load_boston
+house_price=load_boston()
+
+df=pd.DataFrame(house_price.data,columns=house_price.feature_names)
+
+df['PRICE'] = house_price.target
+
+# splitting the dataset into training and test sets with 7:3 ratio along with randomly shuffling
+X_train, X_test, y_train, y_test = train_test_split(df, df['PRICE'], test_size=0.30, random_state=42)
+print(X_train.shape)
+print(X_test.shape)
+print(y_test.shape)
+print(y_train.shape)
+
+# deleting the Price Column from the training part as it is the Output variable
+del X_train['PRICE']
+X_train.shape
+del X_test['PRICE']
+X_test.shape
+
+# normal linear REgression
+reg=LinearRegression().fit(X_train,y_train)
+
+print(reg.coef_)
+print("The intercept values",reg.intercept_)
+
+# here predicting the residuals for the normal regression model.
+predictionlr=reg.predict(X_train)
+residuals=(y_train-predictionlr)
+
+residuals_mean=np.mean(np.abs(residuals))
+lst=list(range(0,len(residuals)))
+# for the training data
+print('for the training data')
+print('R^2:',metrics.r2_score(y_train, predictionlr))
+print('Adjusted R^2:',1 - (1-metrics.r2_score(y_train, predictionlr))*(len(y_train)-1)/(len(y_train)-X_train.shape[1]-1))
+print('MAE:',metrics.mean_absolute_error(y_train, predictionlr))
+print('MSE:',metrics.mean_squared_error(y_train, predictionlr))
+print('RMSE:',np.sqrt(metrics.mean_squared_error(y_train, predictionlr)))
+
+# Predicting Test data with the model
+y_test_pred = reg.predict(X_test)
+# Model Evaluation
+acc_linreg = metrics.r2_score(y_test, y_test_pred)
+print('for the test data')
+print('R^2:', acc_linreg)
+print('Adjusted R^2:',1 - (1-metrics.r2_score(y_test, y_test_pred))*(len(y_test)-1)/(len(y_test)-X_test.shape[1]-1))
+print('MAE:',metrics.mean_absolute_error(y_test, y_test_pred))
+print('MSE:',metrics.mean_squared_error(y_test, y_test_pred))
+print('RMSE:',np.sqrt(metrics.mean_squared_error(y_test, y_test_pred)))
+
+
+
+