Pakistani Politics

 # -*- coding: utf-8 -*- """ Created on Tue Nov 14 13:20:09 2023 @author: Syed Kamran Bukhari """ import numpy as np import pandas as pd import matplotlib.pyplot as plt dataset=pd.read_csv("possum.csv") from sklearn.preprocessing import LabelEncoder label_encoder= LabelEncoder() dataset["sex"]=label_encoder.fit_transform(dataset["sex"]) selected_feature_X = ["Pop", "sex", "age", "chest"] selected_feature_Y = ["belly"] X=dataset[selected_feature_X] Y=dataset[selected_feature_Y] from sklearn.compose import ColumnTransformer from sklearn.preprocessing import OneHotEncoder ct = ColumnTransformer(transformers=[('encoders', OneHotEncoder(),["Pop"])], remainder="passthrough") X=np.array(ct.fit_transform(X)) from sklearn.impute import SimpleImputer imputer = SimpleImputer(missing_values=np.nan , strategy="most_frequent") imputer.fit(X) X=imputer.fi...

 # -*- coding: utf-8 -*- """ Created on Mon Nov 13 21:02:05 2023 @author: Syed Kamran Bukhari """ import numpy as np  import pandas as pd  import matplotlib.pyplot as plt #read csv file dataset = pd.read_csv("possum.csv") from sklearn.preprocessing import LabelEncoder label_encoder= LabelEncoder() dataset["Pop"]=label_encoder.fit_transform(dataset["Pop"]) #feature Selection selected_features_x = ["Pop", "sex", "age", "chest"] selected_features_y = ["belly"] X=dataset[selected_features_x] Y=dataset[selected_features_y] from sklearn.compose import ColumnTransformer from sklearn.preprocessing import OneHotEncoder ct=ColumnTransformer(transformers=[('encoders', OneHotEncoder(),["sex"])], remainder="passthrough") X=np.array(ct.fit_transform(X)) from sklearn.impute import SimpleImputer imputer = SimpleImputer(missing_values=np.nan , strategy="mean...

 # -*- coding: utf-8 -*- """ Created on Thu Nov  9 23:39:19 2023 @author: Syed Kamran Bukhari """ import numpy as np import pandas as pd import matplotlib.pyplot as plt dataset=pd.read_csv("possum.csv") selected_features_x=["Pop", "sex", "age", "chest"] selected_features_y=["belly"] X=dataset[selected_features_x] Y=dataset[selected_features_y] from sklearn.compose import ColumnTransformer from sklearn.preprocessing import OneHotEncoder ct = ColumnTransformer(transformers=[('encoders', OneHotEncoder(),["Pop","sex"])], remainder='passthrough') X=np.array(ct.fit_transform(X)) from sklearn.impute import SimpleImputer imputer=SimpleImputer(missing_values=np.nan,strategy="mean") imputer.fit(X) X=imputer.fit_transform(X) imputer.fit(Y) Y=imputer.fit_transform(Y) from sklearn.preprocessing import PolynomialFeatures poly_reg=PolynomialFeatures(degree=4) poly_re...

 # -*- coding: utf-8 -*- """ Created on Wed Nov  8 10:54:34 2023 @author: Syed Kamran Bukhari """ import numpy as np import pandas as pd import matplotlib.pyplot as plt dataset = pd.read_csv("Position_Salaries.csv") X= dataset.iloc[:,1:2].values Y= dataset.iloc[:,-1].values from sklearn.ensemble import RandomForestRegressor regressor = RandomForestRegressor(n_estimators=90,random_state=0) regressor.fit(X,Y) Y_pred = regressor.predict([[6.5]]) print('The Predicted Salary is = ', Y_pred) #visualizing the data plt.scatter(X,Y, color='red') plt.plot(X,regressor.predict(X), color='blue') plt.xlabel('Salary') plt.ylabel('Salary') plt.title('Position vs Salary') plt.show() #high resolution plot X_grid = np.arange(min(X),max(X),0.1) X_grid=X_grid.reshape(len(X_grid),1) plt.scatter(X,Y, color='red') plt.plot(X_grid,regressor.predict(X_grid), color='blue') plt.xlabel('Salary') plt.ylab...

 # -*- coding: utf-8 -*- """ Created on Wed Nov  8 10:13:03 2023 @author: Syed Kamran Bukhari """ import numpy as np import pandas as pd import matplotlib.pyplot as plt dataset = pd.read_csv("Position_Salaries.csv") X= dataset.iloc[:,1:2].values Y= dataset.iloc[:,-1].values from sklearn.tree import DecisionTreeRegressor regressor = DecisionTreeRegressor(random_state=0) regressor.fit(X,Y) Y_pred = regressor.predict([[6.5]]) print('The Predicted Salary is = ', Y_pred) #visualizing the data plt.scatter(X,Y, color='red') plt.plot(X,regressor.predict(X), color='blue') plt.xlabel('Salary') plt.ylabel('Salary') plt.title('Position vs Salary') plt.show() #high resolution plot X_grid = np.arange(min(X),max(X),0.1) X_grid=X_grid.reshape(len(X_grid),1) plt.scatter(X,Y, color='red') plt.plot(X_grid,regressor.predict(X_grid), color='blue') plt.xlabel('Salary') plt.ylabel('Salary')...

 # -*- coding: utf-8 -*- """ Created on Fri Nov  3 21:43:32 2023 @author: Syed Kamran Bukhari """ import numpy as np import pandas as pd import matplotlib.pyplot as plt #read CSV file dataset = pd.read_csv('train.csv') # Create a boolean mask for rows to be deleted mask = (dataset['SaleCondition'] == 'AdjLand') | (dataset['SaleCondition'] == 'Alloca') | (dataset['SaleCondition'] == 'Family') # Use the mask to filter the DataFrame and keep only rows that don't match the condition dataset = dataset[~mask] # Select relevant features selected_features = ["OverallQual", "GarageCars", "YearBuilt", "PoolArea", "SaleCondition"] #dividing them in to categories in Numbers from sklearn.preprocessing import LabelEncoder label_encoder=LabelEncoder() dataset['SaleCondition']= label_encoder.fit_transform(dataset['SaleCondition']) X = dataset[selec...

 # -*- coding: utf-8 -*- """ Created on Mon Oct 30 13:27:39 2023 @author: Syed Kamran Bukhari """ import numpy as np import pandas as pd import matplotlib.pyplot as plt #Import CSV File dataset = pd.read_csv('Position_Salaries.csv') X= dataset.iloc[:,1:2].values Y=dataset.iloc[:, -1].values from sklearn.preprocessing import StandardScaler sc_x = StandardScaler() X= sc_x.fit_transform(X) sc_y = StandardScaler() Y=Y.reshape(len(Y),1) Y= sc_y.fit_transform(Y) from sklearn.svm import SVR regressor= SVR(kernel='rbf') Y=Y.reshape(len(Y),) regressor.fit(X,Y) Y_pred=regressor.predict(sc_x.transform([[6.5]])) print('The Predicted value of Y is = ',Y_pred) Y_pred=Y_pred.reshape(len(Y_pred), 1) Y_pred= sc_y.inverse_transform(Y_pred) print('The New Predicted value of Y is = ',Y_pred) Y=Y.reshape(len(Y),1) ya = regressor.predict(X) ya = ya.reshape(len(ya),1) plt.scatter(sc_x.inverse_transform(X), sc_y.inverse_transform(Y), color='red...

 # -*- coding: utf-8 -*- """ Created on Mon Oct 23 13:17:55 2023 @author: Syed Kamran Bukhari """ import numpy as np import pandas as pd import matplotlib.pyplot as plt #import CSV file dataset= pd.read_csv('Position_Salaries.csv') X= dataset.iloc[:,1:2].values Y=dataset.iloc[:,-1].values #regressor model from sklearn.linear_model import LinearRegression Linear_regressor = LinearRegression() Linear_regressor.fit(X,Y) #Slope and Intercept print('The Slope is = ', Linear_regressor.coef_) print('The X intercept is = ',Linear_regressor.intercept_) from sklearn.preprocessing import PolynomialFeatures poly_reg =PolynomialFeatures(degree=4) X_poly = poly_reg.fit_transform(X) poly_reg1 =LinearRegression() poly_reg1.fit(X_poly, Y) #linear Regressor Plot plt.scatter(X, Y, color='red') plt.plot(X,Linear_regressor.predict(X),color = 'blue') plt.plot(X,poly_reg1.predict(X_poly), color = 'purple') plt.xlabel('Position...

 # -*- coding: utf-8 -*- """ Created on Wed Oct 18 23:37:32 2023 @author: Syed Kamran Bukhari """ import numpy as np import pandas as pd import matplotlib.pyplot as plt #read CSV file dataset = pd.read_csv('Assi_1_data.csv') #impute mean values in replace of na dataset=dataset[dataset>0] data = np.array(dataset) from sklearn.impute import SimpleImputer Imputer = SimpleImputer(missing_values=np.nan, strategy='median') Imputer.fit(data[:, 0:3]) data[:,0:3]= Imputer.transform(data[:,0:3]) data = pd.DataFrame(data) X=data.iloc[:,-2].values Y=data.iloc[:,-1].values #diving dataset to training and Testing from sklearn.model_selection import train_test_split X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size= 0.2 ,random_state=1) X_train = X_train.reshape(-1, 1) Y_train = Y_train.reshape(-1, 1) X_test = X_test.reshape(-1, 1) Y_test = Y_test.reshape(-1, 1) #linear Regression from sklearn.linear_model import LinearRegression regr...

 # -*- coding: utf-8 -*- """ Created on Mon Oct 16 13:25:06 2023 @author: Syed Kamran Bukhari """ import numpy as np import pandas as pd import matplotlib.pyplot as plt #Load CSV file dataset = pd.read_csv('50_Startups.csv') X= dataset.iloc[:,:-1].values Y= dataset.iloc[:,-1].values #One Hot encoding from sklearn.compose import ColumnTransformer from sklearn.preprocessing import OneHotEncoder ct=ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [3])], remainder ='passthrough') X= np.array(ct.fit_transform(X)) #dividing the dataset to training and Test from sklearn.model_selection import train_test_split X_train,X_test,Y_train,Y_test = train_test_split(X,Y, test_size=0.2, random_state=1) #import Regressor from sklearn.linear_model import LinearRegression regressor=LinearRegression() regressor.fit(X_train, Y_train) #prediction values Y_pred=regressor.predict(X_test) #RMSE score residual = abs(Y_test-Y_pred) ss= residual**2 ...

 # -*- coding: utf-8 -*- """ Created on Sun Oct 22 19:30:20 2023 @author: Syed Kamran Bukhari """ import numpy as np import pandas as pd import matplotlib.pyplot as plt #read CSV file dataset = pd.read_csv('Assi_1_data.csv') #impute mean values in replace of na dataset=dataset[dataset>0] dna= pd.DataFrame(dataset) dna= dna.dropna() dna=np.array(dna) data = np.array(dataset) data1 = np.array(dataset) data2 = np.array(dataset) from sklearn.impute import SimpleImputer Imputer = SimpleImputer(missing_values=np.nan, strategy='most_frequent') Imputer.fit(data[:, 0:3]) data[:,0:3]= Imputer.transform(data[:,0:3]) data = pd.DataFrame(data) Imputer = SimpleImputer(missing_values=np.nan, strategy='median') Imputer.fit(data1[:, 0:3]) data1[:,0:3]= Imputer.transform(data1[:,0:3]) data = pd.DataFrame(data1) Imputer = SimpleImputer(missing_values=np.nan, strategy='mean') Imputer.fit(data2[:, 0:3]) data2[:,0:3]= Imputer.transform(data2[:...