K-Fold Cross Validation

1. Importing Dataset: In[1]: # 'Importing the required Libraries' from os import execl import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.cluster import KMeans import seaborn as sns # 'Importing the dataset using pandas' df=pd.read_csv("auto-mpg_kaggle.csv") # 'Printing the first 5 rows…

Data cleaning on Automobile 1985 dataset and perform descriptive analytics 1. Data Importing: # "Importing the Required libraries" import numpy as np import pandas as pd # 'Importing the Dataset using Pandas' df_auto = pd.read_csv("auto.csv", header = None) # 'Printing the first 5 rows in the Data Frame' df_auto.head()…

Question 1: How does similarity is calculated if data is categorical in nature? Answer: The Similarity Measure is a way of measuring how data samples are related or close to each other. There are Several ways to find the similarity in the categorical in nature. They are mentioned below as, 1. Euclidean. 2. Manhattan.…

Question 1: What is a Neural Network? Answer: A neural network is a series of algorithm that endevors to recognize underlaying relationship in a set of data through a process that mimics the way of human brain operates.   Question 2: What is deep learning? Answer: Deep Learning is a subset of machine learning,…

Question 1: Apply knn to the “Surface defects in stainless steel plates” and identify the differences? # "KNN Coding using Steel plate surface defect data set" # "Importing the Required libraries" import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from sklearn.neighbors…

from math import gamma from typing import Type import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.datasets import load_iris iris=load_iris() print(dir(iris)) print(type(iris)) df=pd.DataFrame(iris.data,columns=iris.feature_names) print(df) df['target']=iris.target print(df) print(df.describe())…

#Importing the Required Libraries from math import gamma from typing import Type import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.datasets import load_iris from sklearn.neighbors import KNeighborsClassifier from sklearn.svm import SVC from sklearn.linear_model import LogisticRegression…

Question 1: Pros and cons of SVM? Pros of SVM: 1. It works really well with a clear margin of separation. 2. It is Effective in high dimensional spaces. 3. It is effective in cases where the number of dimensions is greater than the number of samples. 4. It uses a subset of training points in the decision function (called…

import numpy as np import pandas as pd import seaborn as sns import matplotlib.pyplot as plt from sklearn import linear_model from math import floor import nbconvert from scipy.stats import trim_mean titanic = pd.read_csv("titanic.csv")…

Question 1)Calculate all 4 business moments using pen and paper for the below data set? Answer For Question 1:   Question 2)What is the significance of expected value when simple mean (Sum of all observations/number of observations) is already in place? Answer for Question 2:        Expected Value…

1. Why there is difference in the formula of variance for population and sample? Answer for Question 1:    The Formula for the Population and Sample Variance is as follows, Population Variance:`sigma^2` =(`summation`(Xi-`mux`)^ 2)/N Sample Variance:`S^2` =(`summation`(Xi-`mux`) ^2)/(N-1)    …