As the name suggests, the Supervised Learning definition in Machine Learning is like having a supervisor while a machine learns to carry out tasks. In the process, we basically train the machine with some data that is already labelled correctly. Post this, some new sets of data are given to...
The dependent variable (also known as the response variable or outcome variable) is the variable predicted or explained by the regression model. It is denoted as Y. 2. Independent Variable The independent variable (also known as the predictor or explanatory variable) is the variable used to pred...
pyplot as plt import seaborn as sns # Synthetic Dataset X, y = make_classification(n_samples=1000, n_features=20, n_classes=2, random_state=42) # Split into Training and Test Sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) ...
import seaborn as sns sns.set_style("darkgrid") #Frequency in terms of Hertz fre = 10 #Sample rate fre_samp = 100 t = np.linspace(0, 2, 2 * fre_samp, endpoint = False ) a = np.sin(fre * 2 * np.pi * t) plt.plot(t, a) plt.xlabel('Time (s)') plt.ylabel('Signal a...
import numpy as npimport seaborn as snsimport matplotlib.pyplot as pltdata = np.random.rand(8, 10) # Graph will change with each runfig, ax = plt.subplots(figsize=(10, 6))sns.heatmap(data)plt.title("Random Uniform Data")plt.show() Powered By Treemaps Treemaps are used to repres...
import pandas as pd import sklearn import matplotlib.pyplot as plt import seaborn as sns import numpy from sklearn.cluster import KMeans from sklearn.datasets import make_blobs from sklearn.decomposition import PCA from sklearn.preprocessing import StandardScaler Advantages and disadvantages Advantages...
The code I am using is this one: import numpy as np import pandas as pd import shapefile as shp import matplotlib.pyplot as plt import seaborn as sns sns.set(style="whitegrid", palette="pastel", color_codes=True) sns.mpl.rc("figure", figsize=(10,6)) #opening vector map shp_path ...
import seaborn as snsheatmap = sns.heatmap(rounded_corr_matrix, annot=True) heatmap.set_title('Correlation Heatmap', fontdict={'fontsize':12}, pad=12) A heatmap is a data visualization tool in which a particular phenomenon is mapped to color scales. In our case, darker colors are used...
import seaborn as sns Reading data sets Here, we will use the Iris flower dataset, which is a multivariate and one of the famous datasets available at the UCI machine learning repository. In our data set, we don’t have any missing or misspelled values so we can directly move on ...
import seaborn as sns import matplotlib.pyplot as plt from sklearn.preprocessing import Normalizer from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split from sklearn.metrics import precision_score,recall_score,accuracy_score ...