AJAIRAM

 import pandas as pd from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score,classification_report data = {     'CGPA': ['>=9', '<9', '>=9', '<9', '>=9'],     'Interactivity': ['Yes', 'No', 'No', 'No', 'Yes'],     'Comm': ['Good', 'Moderate', 'Moderate', 'Moderate', 'Moderate'],     'Practical': ['Good', 'Good', 'Average', 'Average', 'Average'],     'JobOffer': ['Yes', 'No', 'Yes', 'No', 'Yes'] } df=pd.DataFrame(data) df.replace({'>=9':1,'<9':0,'Yes':1,'No':0,'Good':1,'Moderate':0,'Average':0},inplace=True) X=df.drop('JobOffer',axis=1) y=df['JobOffer'] X_train,X_test,y...

 import pandas as pd import matplotlib.pyplot as plt from sklearn import tree from sklearn.tree import DecisionTreeClassifier from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score data = {     'Outlook': ['Sunny', 'Sunny', 'Overcast', 'Rain', 'Rain', 'Rain', 'Overcast',                 'Sunny', 'Sunny', 'Rain', 'Sunny', 'Overcast', 'Overcast', 'Rain'],     'Temperature': ['Hot', 'Hot', 'Hot', 'Mild', 'Cool', 'Cool', 'Cool',                     'Mild', 'Cool', 'Mild', 'Mild', 'Mild', 'Hot', 'Mild'],     'Humidity': ['High', 'High', 'High', 'High', 'Normal', 'Normal', 'Normal',                  'High', 'Normal', 'Normal', ...

 import numpy as np def step(x):     return 1 if x>=0 else 0 def train(X,y):     w=np.zeros(X.shape[1])     b=0     for _ in range(5):         print(f'Epoch : {_}')         for i in range(len(X)):             z=np.dot(X[i],w)+b             y_pred=step(z)             error=y[i]-y_pred             w+=error*X[i]             b+=error         print(f'Weights : {w}, Bias : {b}')     return w,b def predict(X,w,b):     return [step(np.dot(x,w)+b) for x in X]  X=np.array([[0,0], [0,1], [1,0], [1,1]]) y=np.array([0,0,0,1]) w,b=train(X,y) print("Prediction:", predict(X,w,b))

import numpy as np import matplotlib.pyplot as plt X=np.array([[1,4],[2,5],[3,8],[4,2]]) Y=np.array([1,6,8,12]) X_bias=np.hstack((np.ones((X.shape[0],1)),X)) beta=np.linalg.inv(X_bias.T.dot(X_bias)).dot(X_bias.T).dot(Y) Y_pred=X_bias.dot(beta) print("Predicted values:",Y_pred) plt.scatter(Y,Y_pred,color='blue',label='Predicted vs Actual') #plt.plot(Y,Y,color='red',linestyle='--',label='Ideal Fit') plt.plot(Y,Y,'r--',label='Ideal Fit') plt.xlabel('Actual X') plt.ylabel('Actual Y') plt.title('Multiple Linear Regression') plt.legend() plt.grid(True) plt.show()

 import numpy as np import matplotlib.pyplot as plt x=np.array([1,2,3,4,5]) y=np.array([1.2,1.8,2.6,3.2,3.8]) xmean=np.mean(x) ymean=np.mean(y) nr=np.sum((x-xmean)*(y-ymean)) dr=np.sum((x-xmean) ** 2) b1=nr/dr b0=ymean-b1*xmean print(f"Estimated coefficients : b0={b0},b1={b1}") ypred_line=b0+b1*x print(f"y_predicted_line : {ypred_line}") print('Prediction for x=7') x_new=7 ypred_new=b0+b1*x_new print(f"Prediction for x :{x_new}, y:{ypred_new}") plt.scatter(x,y,color='blue',label='Original Data') plt.plot(x,ypred_line,color='red',label='Regression Line') plt.xlabel('x') plt.ylabel('y') plt.title('Simple Linear Regression') plt.legend() plt.grid(True) plt.show()