Assnmt 1: Write a python program to Prepare Scatter Plot (Use Forge Dataset / Iris Dataset)
import pandas as pd
import matplotlib.pyplot as plt
iris = pd.read_csv("Iris1.csv")
iris.head()
iris.plot(kind="scatter", x="SepalLengthCm", y="SepalWidthCm")
Assnmt 2: Write a python program the Categorical values in numeric format for a given dataset.

importing pandas as pd

import pandas as pd
#importing data using .read_csv() function
df = pd.read_csv('DataMLcategorical2.csv')
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
label = le.fit_transform(df['Purchased'])

printing label

Label

removing the column 'Purchased' from df

as it is of no use now.

df.drop("Purchased", axis=1, inplace=True)

Appending the array to our dataFrame

with column name 'Purchased'

df["Purchased"] = label

printing Dataframe

df
Assnmt 3: Write a python program to implement simple Linear Regression for predicting salary.
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
dataset = pd.read_csv('Salary_DataSimpleLinearRegression.csv')
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, -1].values
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 1/3, random_state = 0)
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)
y_pred = regressor.predict(X_test)
plt.scatter(X_train, y_train, color = 'red')
plt.plot(X_train, regressor.predict(X_train), color = 'blue')
plt.title('Salary vs Experience (Training set)')
plt.xlabel('Years of Experience')
plt.ylabel('Salary')
plt.show()
Assnmt 4: Write a python program to implement Polynomial Regression for given dataset.
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
dataset = pd.read_csv('Position_SalariesPolynomialRegression.csv')
X = dataset.iloc[:, 1:-1].values
y = dataset.iloc[:, -1].values
from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(X, y)
from sklearn.preprocessing import PolynomialFeatures
poly_reg = PolynomialFeatures(degree = 4)
X_poly = poly_reg.fit_transform(X)
lin_reg_2 = LinearRegression()
lin_reg_2.fit(X_poly, y)
plt.scatter(X, y, color = 'red')
plt.plot(X, lin_reg_2.predict(poly_reg.fit_transform(X)), color = 'blue')
plt.title('Truth or Bluff Polynomial Regression')
plt.xlabel('Position level')
plt.ylabel('Salary')
plt.show()
lin_reg_2.predict(poly_reg.fit_transform([[6.5]]))
Assnmt 5: Write a python program to Implement Naïve Bayes.-No need of dataset
from sklearn.datasets import load iris
iris = load_iris()

store the feature matrix (X) and response vector (y)

X = iris.data
y = iris.target
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=1)
from sklearn.naive_bayes import GaussianNB
gnb = GaussianNB()
gnb.fit(X_train, y_train)
y_pred = gnb.predict(X_test)
from sklearn import metrics
print("Gaussian Naive Bayes model accuracy(in %):", metrics.accuracy_score(y_test, y_pred)*100)
Assnmt 6: Write a python program to implement linear SVM.Datset-Iris1.csv
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn import svm, datasets
iris = pd.read_csv('Iris1.csv')
iris = datasets.load_iris()
X = iris.data[:, :2]
y = iris.target
C = 1.0
svc = svm.SVC(kernel ='linear', C = 1).fit(X, y)
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
h = (x_max / x_min)/100
xx, yy = np.meshgrid(np.arange(x_min, x_max, h),np.arange(y_min, y_max, h))
plt.subplot(1, 1, 1)
Z = svc.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
plt.contourf(xx, yy, Z, cmap = plt.cm.Paired, alpha = 0.8)
plt.scatter(X[:, 0], X[:, 1], c = y, cmap = plt.cm.Paired)
plt.xlabel('Sepal length')
plt.ylabel('Sepal width')
plt.xlim(xx.min(), xx.max())
plt.title('SVC with linear kernel')
plt.show()
Assnmt 7:Write a python program to Implement Decision Tree for a dataset.-Iris1.csv

Python program to implement decision tree algorithm and plot the tree

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn import metrics
import seaborn as sns
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn import tree
iris = load_iris()
data = pd.DataFrame(data = iris.data, columns = iris.feature_names)
data['Species'] = iris.target
target = np.unique(iris.target)
target_n = np.unique(iris.target_names)
target_dict = dict(zip(target, target_n))
data['Species'] = data['Species'].replace(target_dict)
x = data.drop(columns = "Species")
y = data["Species"]
names_features = x.columns
target_labels = y.unique()
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.3, random_state = 93)
from sklearn.tree import DecisionTreeClassifier
dtc = DecisionTreeClassifier(max_depth = 3, random_state = 93)
dtc.fit(x_train, y_train)
plt.figure(figsize = (30, 10), facecolor = 'b')
Tree = tree.plot_tree(dtc, feature_names = names_features, class_names = target_labels, rounded =
True, filled = True, fontsize = 14)
plt.show()
y_pred = dtc.predict(x_test)
Assnmt 8: Write a python program to implement Agglomerative clustering on a dataset.
import numpy as nm
import matplotlib.pyplot as mtp
import pandas as pd
dataset = pd.read_csv("ML_8_Mall_Customers.csv")
x = dataset.iloc[:, [3, 4]].values
import scipy.cluster.hierarchy as shc
dendro = shc.dendrogram(shc.linkage(x, method="ward"))
mtp.title("Dendrogrma Plot")
mtp.ylabel("Euclidean Distances")
mtp.xlabel("Customers")
mtp.show()
from sklearn.cluster import AgglomerativeClustering
hc= AgglomerativeClustering(n_clusters=5, affinity='euclidean', linkage='ward')
y_pred= hc.fit_predict(x)
mtp.scatter(x[y_pred == 0, 0], x[y_pred == 0, 1], s = 100, c = 'blue', label = 'Cluster 1')
mtp.scatter(x[y_pred == 1, 0], x[y_pred == 1, 1], s = 100, c = 'green', label = 'Cluster 2')
mtp.scatter(x[y_pred== 2, 0], x[y_pred == 2, 1], s = 100, c = 'red', label = 'Cluster 3')
mtp.scatter(x[y_pred == 3, 0], x[y_pred == 3, 1], s = 100, c = 'cyan', label = 'Cluster 4')
mtp.scatter(x[y_pred == 4, 0], x[y_pred == 4, 1], s = 100, c = 'magenta', label = 'Cluster 5')
mtp.title('Clusters of customers')
mtp.xlabel('Annual Income (k$)')
mtp.ylabel('Spending Score (1-100)')
mtp.legend()
mtp.show()