import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.linear_model import LinearRegression

df = pd.read_csv("/work/BIRTHWT/birthwt.csv")

def calc_covariance(dataset1,dataset2): ''' Def : Covariance measures the relationship trend between two sets of data. Formula : 1) Σ((X - X_mean)*(Y - Y_mean)) ∕ n ''' mean1 = np.mean(dataset1) mean2 = np.mean(dataset2) return np.sum(np.multiply(dataset1-mean1,dataset2-mean2))/len(dataset1)

def correlation(dataset1,dataset2): ''' Def : Covariance measures the relationship trend between two sets of data. Formula : 1) cov(x,y)/(std(x)*std(y)) ''' cov =calc_covariance(dataset1,dataset2) sd1 = np.std(dataset1) sd2 = np.std(dataset2) return cov/(sd1*sd2)

# Age of mother age = df["age"] age = age.to_numpy() # Birth weight in grams birthwt = df["bwt"] birthwt = birthwt.to_numpy()

correlation(age, birthwt)

# Converting birth weight from gram to kg for better scaling plt.scatter(age,birthwt/1000,c ="green") plt.xlabel("Age") plt.ylabel("Birth weight(Kg)")

lr = LinearRegression() age = age.reshape(-1,1) lr.fit(age,birthwt)

y = lr.predict(age) print("Coefficients :",lr.coef_[0]) print("intercept :",lr.intercept_)

plt.plot(age,y,color= "red") plt.scatter(age,birthwt,c= "green") plt.xlabel("Age") plt.ylabel("Birth weight(g)") plt.show()

# Mother's weight during last menstrual period.(in pounds) motherswt = df["lwt"] motherswt =motherswt.to_numpy() # converting in grams to pounds birthwt = birthwt/454

correlation(motherswt,birthwt)

plt.xlabel("Mother's weight") plt.ylabel("Birth weight (Kg)") plt.scatter(motherswt,birthwt,c = "darkblue")

motherswt =motherswt.reshape(-1,1) lr.fit(motherswt,birthwt)

z = lr.predict(motherswt) print("Coefficients :",lr.coef_[0]) print("intercept :",lr.intercept_)

plt.plot(motherswt,z,c="orange") plt.scatter(motherswt,birthwt,c ="darkblue") plt.xlabel("Mother's weight") plt.ylabel("Birth weight") plt.show()